# Radiation Testing of Optical and Semiconductor Components for Radiation-
Tolerant LED Luminaires

Alessandro Floriduz, , and James D. Devine A. Floriduz and J. D. Devine are
with the Electrical Network Projects Section of the Electrical Group,
Engineering Department (EN-EL), CERN (European Organization for Nuclear
Research), CH-1211 Geneva 23, Switzerland (email:
<EMAIL_ADDRESS>james.dilwyn.devine@cern.ch).

###### Abstract

An irradiation campaign was conducted to provide guidance in the selection of
materials and components for the radiation hardening of LED lights for use in
CERN accelerator tunnels. This work describes the effects of gamma-rays on
commercial-grade borosilicate, fused quartz, polymethylmethacrylate, and
polycarbonate samples up to doses of 100 kGy, to qualify their use as optical
materials in rad-hard LED-based luminaires. In addition, a Si bridge rectifier
and a SiC Junction Barrier Schottky diode for use in power supplies of rad-
hard LED lighting systems are tested using 24 GeV/_c_ protons. The physical
degradation mechanisms are discussed for each element.

###### Keywords:

Radiation testing, lighting system, fused quartz, borosilicate, polycarbonate,
polymethylmethacrylate, SiC Schottky diode, silicon diode bridge, defects,
colour centres.

##  I Introduction

GENERAL lighting in CERN accelerator tunnels and experimental caverns is
currently provided by fluorescent tubes with wire-wound ballasts, and the
present emergency lighting system comprises a combination of incandescent and
low-pressure sodium discharge lamps. However, luminaires using these
technologies are now obsolete and cannot be purchased any more on the European
market, and thus have to be replaced by LED lights having much higher
efficiency. Nevertheless, adoption of LED-based luminaires in radiation
environments like high-energy physics accelerator facilities poses several
challenges due to the radiation effects on semiconductor components [1];
indeed, we recall that, on the walls of a typical section of CERN accelerator
tunnels, the annual radiation levels can exceed a 1 MeV neutron equivalent
(neq) fluence in Si of 5$\times$10${}^{\text{12}}$ n/cm${}^{\text{2}}$ and a
dose of 1 kGy [2]. Irradiation of prototype LED lights for radiation
environment has been carried out previously, results of which are summarized
in [1]. Following the encouraging outcome of these preliminary tests, a
dedicated irradiation campaign has been performed to study the effects of
radiation on each individual component to be included in a radiation-tolerant
LED lamp (see Fig. 1), namely: _i)_ the diodes for the power supply unit
(consisting of a simple bridge rectifier), _ii)_ the plastic lenses and glass
windows, and _iii)_ the GaN-based white LEDs. This paper summarizes the
results on irradiation tests of the first two components; the behaviour of
irradiated high-power white GaN LEDs is available in [3].

In this work, we tested under $\gamma$-ray irradiation samples of borosilicate
(BS), fused quartz (FQ), polymethylmethacrylate (PMMA) and polycarbonate (PC).
We chose to study these materials as they are commonly used in commercial
luminaires: glass (BS or FQ) for protective windows, and plastic (PMMA or PC)
for secondary optics. Reports on the irradiation of these materials already
exist, but tests under the same irradiation and annealing conditions were
required to better compare the amount of radiation damage induced to the
various materials, so as to identify the most resistant ones.

In addition, a Si bridge rectifier and a SiC Junction Barrier Schottky (JBS)
diode have been tested; irradiation was done using 24 GeV/_c_ protons instead
of $\gamma$-rays, displacement damage being the dominant degradation mechanism
in diodes. A Si diode bridge sharing the same technology as the selected one
had already been tested against irradiation up to a 1 MeV neq fluence of
8$\times$10${}^{\text{13}}$ n/cm${}^{\text{2}}$ [4]; in the present work, the
selected Si diodes were studied under even higher fluences. A SiC JBS diode
was characterised for comparison and also because no irradiation of SiC JBS
diodes with high-energy protons has so far been reported.

##  II Components under test

###  II-A Optical components

Samples of borosilicate (BS), fused quartz (FQ), UV-grade
polymethylmethacrylate (PMMA), and polycarbonate (PC) have been tested against
$\gamma$-ray irradiation. The samples have been provided by Goodfellow
Cambridge Ltd. (UK); manufacturer part references are: LS475409/7, LS475409/3,
LS475409/12, LS475409/13 for BS, FQ, PMMA, and PC samples, respectively. All
samples were in form of polished disks with diameter of 40 mm and thickness of
3 mm (except BS and PMMA samples, having thickness of 3.3 and 3.1 mm
respectively). All materials under test are commercial-grade and not
specifically produced for use in radiation environment.

###  II-B Diodes

Silicon bridge rectifiers and SiC Junction Barrier Schottky (JBS) diodes have
been characterized against displacement damage using 24 GeV/_c_ protons, to
qualify their use in rad-hard AC/DC power supplies for LED lights. The Si
diode bridge under test is a Vishay B380C1000504H glass-passivated single-
phase bridge rectifier in a WOG package, rated 1 A in average forward current
and 600 V in maximum repetitive peak reverse voltage. It shares the same
technology as the Vishay GBU8K diode bridge [5, 6], which has been previously
tested against 1 MeV neq fluences up to 8$\times$10${}^{\text{13}}$
n/cm${}^{\text{2}}$ [4]; for this reason, it was selected as a candidate
component. Two types of 4H-SiC JBS diodes have also been tested: STPSC10H065D
and STPSC10H12-Y diodes from STMicroelectronics, rated 650 V and 1.2 kV in
maximum repetitive peak reverse voltage, respectively; they are both rated for
10 A average forward current. More information on their device structures is
available in [7]. The TO-220 package variants were used to facilitate testing.

##  III Irradiation conditions

###  III-A $\gamma$-ray irradiation

$\gamma$-ray irradiation of optical components was performed at Ionisos
(Dagneux, France) [8], using a ${}^{\text{60}}$Co source. Irradiation was done
in air, at room temperature and atmospheric pressure. Four different target
doses were specified: 2, 25, 50, and 100 kGy; the actual values reached (as
measured by radiochromic dosimeters) are respectively 2.5, 28.5, 58.2, and
113.1 kGy. For the sake of brevity, we will make reference to the nominal
values in the remainder of this paper. The doses are specified with respect to
water (i.e. in kGy[H2O]). Table I illustrates the number of samples tested at
each dose. During irradiation, the samples were contained in stainless steel
perforated baskets. Irradiation of samples with target dose of 2 kGy was
performed through 5 complete turns on a secondary tray conveyor around the
${}^{\text{60}}$Co source, at a dose rate comprised between 0.5–1 kGy/h.
Samples with 25, 50, and 100 kGy target doses were irradiated statically (i.e.
placed on metallic shelves facing the source), at average dose rates of 0.46
kGy/h, 0.6 kGy/h, and 0.58 kGy/h, respectively. Irradiation of samples with
the highest target doses started first, so that irradiation at all doses
finished simultaneously, avoiding different annealing times for different
target doses. The irradiated samples were stored at room temperature, and were
all characterized within 50 to 52 hours from completion of irradiation.

Figure 1: Block diagram model of an LED luminaire, which comprises a power supply unit (PSU) feeding a string of white LEDs; it also includes plastic lenses for improved light distribution and a glass window for protection reasons. The response to radiation of the components highlighted in red is illustrated in the present work. TABLE I: Number of samples irradiated per dose Material | 2 kGy | 25 kGy | 50 kGy | 100 kGy
---|---|---|---|---
BS | 1 pc. | 1 pc. | – | 1 pc.
FQ | 1 pc. | 1 pc. | – | 1 pc.
PMMA | 4 pcs. | 4 pcs. | 4 pcs. | 4 pcs.
PC | 4 pcs. | 4 pcs. | 4 pcs. | 4 pcs.

###  III-B 24 GeV/_c_ proton irradiation

Irradiation of samples of Si and SiC diodes was done at IRRAD facility (CERN)
[9], using a 24 GeV/_c_ proton beam having a nominal gaussian profile with a
FWHM of 1.2$\times$1.2 cm${}^{\text{2}}$. The actual proton fluence was
determined by measuring the activation of thin aluminium foils placed in front
of the samples during irradiation. A fluence of 24 GeV/_c_ protons can be
converted into a 1 MeV neq fluence providing the same non-ionising energy loss
in silicon through the factor of 0.58 [10]. Irradiation was done at room
temperature and atmospheric pressure. The samples were not powered while
irradiated (passive test). Two batches of both Si and SiC diodes were
irradiated; the target and actual fluences are collected in Table II. Each
batch comprises 5 diodes, except batch #1 of Si diodes, which included only 3
units. After irradiation, the diodes were stored at ambient temperature.
Measurements of irradiated diodes were taken only upon authorisation of the
irradiation facility personnel, when the residual activation of the samples
was acceptably low. The time before measurement (i.e. annealing time) for
batches #1 and #2 of Si diodes was 6 and 5 days, respectively; for SiC diodes,
it was 20 and 38 days, respectively. Radiation damage in 4H-SiC is known to be
thermally stable at room temperature [11] and should not be affected by the
longer annealing period, although some form of recovery (associated to
unstable radiation-induced defects) may not be excluded [12].

TABLE II: Irradiation fluences of Si and SiC diodes | Device
---
under test
| Target 24 GeV/_c_
---
fluence
| Actual 24 GeV/_c_
---
fluence
| Actual 1 MeV
---
neq fluence in Si
| Si diodes
---
(batch #1)
8$\times$10${}^{\text{12}}$ p/cm${}^{\text{2}}$ | 9.5$\times$10${}^{\text{12}}$ p/cm${}^{\text{2}}$ | 5.5$\times$10${}^{\text{12}}$ n/cm${}^{\text{2}}$
| Si diodes
---
(batch #2)
3$\times$10${}^{\text{14}}$ p/cm${}^{\text{2}}$ | 2.4$\times$10${}^{\text{14}}$ p/cm${}^{\text{2}}$ | 1.4$\times$10${}^{\text{14}}$ n/cm${}^{\text{2}}$
| SiC diodes
---
(batch #1)
10${}^{\text{13}}$ p/cm${}^{\text{2}}$ | 1.2$\times$10${}^{\text{13}}$ p/cm${}^{\text{2}}$ | 7$\times$10${}^{\text{12}}$ n/cm${}^{\text{2}}$
| SiC diodes
---
(batch #2)
2$\times$10${}^{\text{14}}$ p/cm${}^{\text{2}}$ | 1.9$\times$10${}^{\text{14}}$ p/cm${}^{\text{2}}$ | 1.1$\times$10${}^{\text{14}}$ n/cm${}^{\text{2}}$

##  IV Experimental results

###  IV-A Optical components

Transmission spectra of all samples of optical material were recorded before
and after irradiation using a Perkin-Elmer UV-VIS Lambda 650 spectrophotometer
equipped with a 150 mm integrating sphere in the range 300–800 nm (only for
quartz samples: 200–800 nm). Figure 2 illustrates the transmission spectra
before and after $\gamma$-ray exposure. Equivalent results were obtained for
all PMMA and PC samples irradiated to the same dose, so data relative to only
one sample per dose is presented for these materials.

(a)

(b)

(c)

(d)

Figure 2: Transmission spectra before and after $\gamma$-ray irradiation of
samples of: (a) borosilicate, (b) fused quartz, (c) PMMA, (d) polycarbonate.

Before irradiation, transmittance of BS is $>$90% (see Fig. 2a) until the UV
cut-off edge at 335 nm [13, 14]. Upon increasing dose, two strong absorption
bands located near 330 nm and 475 nm appear; these bands have been previously
attributed to boron oxygen hole centres [15] (each centre is given by a hole
trapped on a bridge oxygen structure with [BO4]-). The large increase of
absorption in the UV-blue portion of the spectrum leads to the darkening of BS
samples (see Fig. 3a).

All samples of FQ before irradiation exhibit high transmittance ($>$90%) until
260 nm (see Fig. 2b). Before irradiation, an absorption band located at 240 nm
is clearly apparent, which we identify with the $B_{2}\beta$ band due to Ge
oxygen deficient centres (GeODC) [16, 17]; this band precedes the UV cut-off
edge, occurring at values close to those reported in literature for other
samples of FQ [16, 17]. After irradiation, two new absorption bands emerge in
the UV: the first at $\sim$210 nm, the second at 300 nm. The first band is
attributed to the E′ centre (Si dangling bond $\equiv$Si$\cdot$) [16, 17];
with increasing dose, absorption at E′ centres increases steadily, and
slightly shifts to longer wavelengths. The origin of the absorption band at
300 nm, referred to in literature as the $B_{1}$ band, is still unclear [16,
17], although it is thought to be formed from the conversion of the
$B_{2}\beta$ band: the proposed mechanism is that a GeODC loses an electron
due to irradiation creating a centre absorbing at 300 nm [16]. It has been
demonstrated in previous works [16, 18] that the emergence of the $B_{1}$ band
upon irradiation occurs only in FQ, and not in crystalline quartz or synthetic
fused silica. Absorption at this band increases with increasing dose up to 25
kGy; however, it decreases after 100 kGy. In previous works on the irradiation
of FQ using neutrons and x-rays [18, 19, 20, 21], it was shown that, in some
specimens of FQ, the $B_{1}$ band initially increased with increasing
radiation exposure, but after reaching a maximum, it started to decrease with
further irradiation (this phenomenon is termed radiation bleaching). A
detailed analysis of this phenomenon was performed in [20], where radiation
bleaching of the $B_{1}$ band was explained in terms of interdependence
between the occupancy of $B_{1}$ centres (i.e. the traps responsible for the
$B_{1}$ band) and the occupancy of the other defects present in irradiated FQ.
We therefore attribute the decrease in absorption of the $B_{1}$ band observed
in our sample irradiated at 100 kGy to the same phenomenon already observed in
the case of neutron and x-rays irradiation. In any case, the FQ samples stay
transparent in the visible range even after the highest dose, as can be
visually seen from Fig. 3b.

The transmittance curves of $\gamma$-irradiated UV-grade PMMA samples are
shown in Fig. 2c. Upon irradiation, a strong increase in UV absorption occurs,
and two absorption bands between 385–425 nm and 515–580 nm emerge. Increased
absorption in the UV has been attributed to increased density of dienes group
(–C$=$C–)n due to radiation [22]. The two bands in the visible are due to
radiation-induced free radicals acting as colour centres [22, 23, 24];
$\gamma$-ray irradiation involves scission of C$=$O, C–C, C–O–C, and C–C–O
bonds [22]. It is also evident that radiation-induced optical damage tends to
saturate at higher doses (the curves after 50 and 100 kGy are almost
overlapping); this phenomenon is due to competition between molecular scission
and cross-linking induced by radiation [22]. Previous studies in literature
have shown that cross-linking under $\gamma$-ray irradiation occurs at doses
$>$10 kGy, and is initiated by the absorption of water from airborne moisture
[22]. Cross-linking occurs between C–H, O–H, and H–O–H bonds (the last two
induced by absorbed water molecules) [22]. As a consequence of the
aforementioned colour centres, PMMA samples turned yellow upon irradiation
(see Fig. 3c).

Finally, the transmittance curves of PC samples are shown in Fig. 2d. Several
absorption bands are introduced by radiation in the visible range, the most
evident ones being around $\sim$415 nm and between 550–580 nm. Previous works
[25, 26, 27, 28] have shown that $\gamma$-ray irradiation of PC leads to chain
scissioning and formation of different types of products (like e.g. species
containing ortho-quinone groups), acting as colour centres in the visible
spectrum. Unlike PMMA, absorption at these radiation-induced bands does not
saturate at high doses (conversely, the optical damage continues to increase
with dose). A larger reduction in trasmittance is obtained in PC than in PMMA,
as also demonstrated by the more intense yellow radiation-induced colouration
of the PC samples (see Fig. 3d).

(a)

(b)

(c)

(d)

Figure 3: Pictures of irradiated samples of: (a) borosilicate, (b) fused
quartz, (c) PMMA, (d) polycarbonate.

Radiation-induced absorption bands in the considered materials decrease if
subjected to thermal treatment with high temperatures (i.e. radiation-induced
defects originating these bands can be thermally annealed); in particular,
previous experiments have shown that recovery of transmission at radiation-
induced bands occurs even at room temperature. For more details on thermal
annealing at room temperature in air of borosilicate, we defer to [13, 29];
for quartz, we defer to [30] and references therein; for PMMA and PC, we make
reference to [23, 31]. In addition, radiation-induced absorption bands in
these materials can also be optically bleached: absorption bands at 210 nm and
300 nm in $\gamma$-ray irradiated FQ are known to photobleach under UV light
[21]; visible light can bleach radiation-induced absorption bands in both BS
[32, 33] and PC [34, 35]; optical bleaching of PMMA presents a more complex
behaviour, exhibiting a reduction or an enhancement of the radiation-induced
absorption in the UV spectrum depending on the emission wavelength of the
light source used for bleaching [36]. Therefore, keeping in mind the
application of these materials as optical components for rad-hard lighting
systems, a recovery of the transmission properties is to be expected after
irradiation as a consequence of thermal annealing (even at room temperature)
and, particularly in BS and PC, optical bleaching induced by the white LEDs
(for reference, the electroluminescence spectrum of a white GaN LED extends
from 400 nm to 750 nm). Nevertheless, in the design of a rad-hard luminaire,
the optical components should be chosen based on their transmission properties
just after irradiation (i.e. based on the data presented in the paragraphs
above), as this would represent the worst-case (most-conservative) scenario.
For example, access by human operators into CERN experimental facilities
tunnels is prohibited while the particle accelerators are running, owing to
the extremely high radiation levels produced by the circulating beams [2];
under such circumstances, the tunnel illumination system is therefore not
required to be activated (i.e. lights are off while the accelerators are
running). Access to accelerator tunnels is possible only during machine stops,
when there is no circulating beam, after a cool down period to allow the
residual radiation to decay (typically a few hours from the beam stop, but it
can be 30–40 hours for the most radioactive areas). Consequently, the lighting
system shall be designed to provide the stipulated minimum illuminance levels
just after the accelerator stops, so that the optical materials offering the
best performance just after irradiation should be selected for use in rad-hard
luminaires.

In conclusion, BS exhibits a significant reduction of visible light
transmission even after 2 kGy (corresponding to $\sim$2 years on the tunnel
walls of a typical section of CERN accelerator complex), while FQ retains
transparency to visible light even after 100 kGy; therefore, BS protective
windows in luminaires for radioactive environment should be avoided in favour
to FQ components. Visible light transmission in both PMMA and PC is degraded
by radiation-induced colour centres, but larger values of absorption are
measured in PC samples; consequently PMMA should be specified instead of PC as
a component for secondary optics lenses.

###  IV-B Diodes

Current-voltage (_I_ – _V_) characteristics of Si and SiC diodes were measured
before and after irradiation with a Keithley 2410 source-meter unit, at room
temperature. Measurements of the forward and reverse _I_ – _V_ curves were
done in the range 0–1 A, and $-$20–0 V, respectively. In the case of Si bridge
rectifiers, all four diodes contained in each package were tested. Equivalent
results were obtained for all diodes irradiated in the same batch; for this
reason, data of only one representative sample per fluence is shown. All
fluences are given as 1 MeV neq fluences in Si.

(a)

(b)

Figure 4: _I_ – _V_ characteristics at room temperature of (a) STPSC10H065D
and (b) STPSC10H12-Y SiC diodes, before and after irradiation (expressed as 1
MeV neq fluence).

(a)

(b)

Figure 5: Experimental and fitted _I_ – _V_ characteristics at room
temperature of (a) STPSC10H065D and (b) STPSC10H12-Y SiC diodes, before and
after irradiation (expressed as 1 MeV neq fluence).

Figures 4a and 4b show the forward _I_ – _V_ curves before and after
irradiation of 650 V and 1200 V SiC diodes, respectively. The behaviour of
irradiated 650 V and 1200 V SiC diodes was found to be similar: degradation
after 7$\times$10${}^{\text{12}}$ n/cm${}^{\text{2}}$ is very small, while
larger damage is detected after the highest fluence (in particular, for
currents above 0.1 A). We found that _I_ – _V_ curves of both 650 V and 1200 V
SiC diodes before irradiation and after a fluence of
7$\times$10${}^{\text{12}}$ n/cm${}^{\text{2}}$ can be well described by the
standard model of an ideal diode with a series resistance [37]:

$I=I_{s}\left\\{\exp\left[\frac{q(V-R_{S}I)}{\eta k_{B}T}\right]-1\right\\},$
(1)

where $I_{s}$ is the saturation current, $q$ is the absolute electronic
charge, $\eta$ is the ideality factor, $k_{B}$ is the Boltzmann constant, $T$
is the absolute temperature, and $R_{S}$ denotes the series resistance. On the
other hand, after the highest fluence, two segments with different slopes are
observed in the _I_ – _V_ characteristics instead of a single exponential
behaviour as in (1); these two segments represent two different current
regimes (i.e. different conduction mechanisms), each characterised by a
distinct ideality factor $\eta$. In this case, the _I_ – _V_ curves of SiC
diodes irradiated at 1.1$\times$10${}^{\text{14}}$ n/cm${}^{\text{2}}$ can be
expressed as:

$I=\begin{cases}I_{s_{1}}\left[\exp\left(qV^{\prime}/\eta_{1}k_{B}T\right)-1\right],\quad
V<V_{T}\\\
I_{s_{2}}\left[\exp\left(qV^{\prime}/\eta_{2}k_{B}T\right)-1\right],\quad
V>V_{T}\end{cases}$ (2)

where $V^{\prime}=V-R_{S}I$, and $V_{T}$ denotes the voltage at which the
transition between the two current regimes occurs. For all 650 V and 1200 V
SiC diodes tested, $V_{T}$ is about 0.6 V.

The experimental _I_ – _V_ characteristics of SiC diodes were fitted to their
corresponding models (1) or (2). Before irradiation and after
7$\times$10${}^{\text{12}}$ n/cm${}^{\text{2}}$, the values of $\eta$ and
$I_{s}$ in (1) have been extracted through an exponential fit in the current
range from 5$\times$10${}^{-\text{8}}$ A to 5$\times$10${}^{-\text{4}}$ A.
After 1.1$\times$10${}^{\text{14}}$ n/cm${}^{\text{2}}$, the saturation
current and ideality factor for the first (respectively, second) current
regime in (2) are obtained by exponential fitting of experimental _I_ – _V_
curves in the current interval from 10${}^{-\text{9}}$ A to 10${}^{-\text{6}}$
A (respectively, from 5$\times$10${}^{-\text{6}}$ A to
5$\times$10${}^{-\text{4}}$ A). Analogously to [37], $R_{S}$ in (1) and (2)
was disregarded while performing these exponential fits, since its effect is
negligible in the considered current intervals. $R_{S}$ was then extracted for
all curves by fitting the experimental _I_ – _V_ characteristics for $I>$0.5
A. Figures 5a and 5b illustrate the experimental and fitted _I_ – _V_ curves
for 650 V and 1200 V SiC diodes, respectively; for clarity reasons (i.e. to
allow for a distinct representation of all curves), the data is shown only in
a limited interval (0.5–0.66 V), which nevertheless spans across almost three
orders of magnitude in current. For _I_ – _V_ curves after
1.1$\times$10${}^{\text{14}}$ n/cm${}^{\text{2}}$, the first and second
current regimes are also indicated. As can be seen, fitted and experimental
data are in excellent agreement in the represented interval.

TABLE III: Relative variation of $I_{s}$, $\eta$, and $R_{S}$ in SiC diodes upon high-energy proton irradiation | Voltage
---
rating
| 1 MeV neq
---
fluence in Si
| $\Delta I_{s}$
---
$\left(\Delta I_{s_{1}}\right)$
| $\Delta\eta$
---
$\left(\Delta\eta_{1}\right)$
$\Delta R_{S}$
650 V |  | 7$\times$10${}^{\text{12}}$ n/cm${}^{\text{2}}$
---
$-$16% |  $+$0.03% |  $+$8%
650 V |  | 1.1$\times$10${}^{\text{14}}$ n/cm${}^{\text{2}}$
---
$-$33% |  $+$0.13% |  $+$105%
1200 V |  | 7$\times$10${}^{\text{12}}$ n/cm${}^{\text{2}}$
---
$-$21% |  $-$0.38% |  $+$9%
1200 V |  | 1.1$\times$10${}^{\text{14}}$ n/cm${}^{\text{2}}$
---
$-$24% |  $+$0.27% |  $+$143%

Radiation introduces in 4H-SiC a wide range of defects, predominantly
vacancies and interstitials of Si and C atoms, and combinations thereof (due
to low mobility of primary radiation defects, formation of defect complexes
including impurities is limited) [38]. These defects act as compensation and
trapping centres, leading to a reduction in free electron density (carrier
removal) and hence to greater series resistance $R_{S}$ and lower values of
saturation current $I_{s}$ [39, 40, 38]. Such variations of $I_{s}$ and
$R_{S}$ are apparent in Figs. 4a and 4b, and can be quantitatively analysed by
studying the evolution with increasing radiation exposure of the fitted values
of $I_{s}$, $\eta$ and $R_{S}$ in models (1) and (2). The relative variations
of these parameters upon irradiation are collected in Table III; the change in
saturation current and ideality factor after the highest fluence is computed
with respect to values of the first current regime, i.e. $I_{s_{1}}$ and
$\eta_{1}$. All variations are computed as $(X_{a}-X_{b})/X_{b}$, where
$X_{b}$ and $X_{a}$ denote the same quantity before and after irradiation,
respectively. After a fluence of 7$\times$10${}^{\text{12}}$
n/cm${}^{\text{2}}$, a reduction in $I_{s}$ of $-$16% and an increase in
$R_{S}$ of 8% w.r.t. pre-irradiated samples is found in 650 V SiC diodes; in
1200 V diodes, $I_{s}$ decreases by $-$21% and $R_{S}$ increases by 9%. In
addition, only marginal variations in $\eta$ upon irradiation are found in
both types of SiC diodes ($\eta\approx\text{1.074}$ and
$\eta\approx\text{1.04}$ for 650 V and 1200 V diodes, respectively, both
before and after irradiation). Hence, modest carrier removal is already
detected after 7$\times$10${}^{\text{12}}$ n/cm${}^{\text{2}}$, although its
impact on the electrical characteristics of the diodes is negligible, as
apparent from Figs. 4 and 5. After irradiation at
1.1$\times$10${}^{\text{14}}$ n/cm${}^{\text{2}}$, $R_{S}$ in 650 V diodes
increases by $\sim$105% w.r.t. pre-irradiated samples, changing from 95
m$\Omega$ (before irradiation) to 195 m$\Omega$ (after
1.1$\times$10${}^{\text{14}}$ n/cm${}^{\text{2}}$); $R_{S}$ in 1200 V diodes
degrades even more, increasing by $\sim$143% from 74 m$\Omega$ (before
irradiation) to 180 m$\Omega$ (after 1.1$\times$10${}^{\text{14}}$
n/cm${}^{\text{2}}$). This increase in $R_{S}$ is responsible for the
deviations observed in the _I_ – _V_ curves at currents $I>$0.1 A after the
highest fluence (see Figs. 4a and 4b). The larger increase of $R_{S}$ in 1200
V devices is attributed to different device geometry (like e.g. larger
vertical thickness of the n-drift region). In any case, this increase in
$R_{S}$ has only a limited effect on the electrical characteristics of SiC
diodes for currents up to 1 A. The ideality factor of the first region
($\eta_{1}$) after 1.1$\times$10${}^{\text{14}}$ n/cm${}^{\text{2}}$ is nearly
identical to its corresponding pre-irradiation values in both 650 V and 1200 V
diodes; in addition, $I_{s_{1}}$ decreases by $-$33% and $-$24% w.r.t. pre-
irradiated samples in 650 V and 1200 V diodes, respectively. Concerning the
second current regime, the values of $\eta_{2}$ are higher than those of
$\eta_{1}$: $\eta_{2}=\text{1.126}$ and $\eta_{2}=\text{1.07}$ in 650 V and
1200 V diodes, respectively, i.e. they are 6.1% and 3.1% larger than their
corresponding $\eta_{1}$ values; a slight increase of the ideality factor upon
proton irradiation of 4H-SiC JBS diodes was also found in [37]. We attribute
the emergence of the second current regime to radiation-induced defects. In
addition, we ascribe the slightly different behaviour of $I_{s_{1}}$ and
$\eta_{2}$ between 650 V and 1200 V diodes after 1.1$\times$10${}^{\text{14}}$
n/cm${}^{\text{2}}$ to different device geometry and doping characteristics,
leading to marginally dissimilar current conduction mechanisms. Our results in
terms of device degradation are in good agreement with those in [40, 37],
despite use of a diode from a different manufacturer and with different
ratings (normalization of radiation damage at various proton energies is done
via NIEL curves for SiC obtained using screened Coulomb potential [41, 42] and
considering the effect of nuclear interactions dominating at energies $>$100
MeV [43]). Finally, no variations are detected in the reverse bias curves, as
expected for Schottky-based diodes and as already noted in literature for
similar devices tested at comparable fluences [39, 37]. All these results show
that the 4H-SiC JBS diodes under test present only a limited degradation for
currents up to 1 A after fluences of 1.1$\times$10${}^{\text{14}}$
n/cm${}^{\text{2}}$, and hence are expected to reliably withstand the
radiation environment of a typical section of CERN accelerator tunnels [2] for
at least 10 years.

(a)

(b)

Figure 6: (a) Forward and (b) reverse _I_ – _V_ characteristics at room
temperature of Si diodes of B380C bridge rectifiers, before and after
irradiation (expressed as 1 MeV neq fluence).

The forward and reverse _I_ – _V_ curves of B380C diodes before and after
irradiation are illustrated in Figs. 6a and 6b, respectively. The four diodes
of each bridge exhibited similar characteristics, so only data of individual
diodes is presented. High-energy proton irradiation introduces numerous
defects in Si including vacancies, interstitials, divacancies,
diinterstitials, vacancy-oxygen centres, C interstitials (C${}_{\text{i}}$),
complexes of O, C${}_{\text{i}}$, H and Si vacancies and interstitials, as
well as vacancy clusters [44]. These defects act as generation-recombination
centres, traps or compensation centres, resulting in increased recombination
current at low forward voltages, and increased series resistance due to
carrier removal [45, 4, 39]. These effects are clearly observed in Fig. 6a. An
increase of generation current at reverse bias is also measured: current at
$-$20 V increases from 2.6$\times$10${}^{-\text{8}}$ A before irradiation to
4.9$\times$10${}^{-\text{8}}$ A after 5.5$\times$10${}^{\text{12}}$
n/cm${}^{\text{2}}$, and to 9.1$\times$10${}^{-\text{7}}$ A after
1.4$\times$10${}^{\text{14}}$ n/cm${}^{\text{2}}$ (see Fig. 6b). It is worth
noting that Si diodes exhibit significant degradation at fluences more than an
order of magnitude lower than SiC JBS diodes.

##  V Conclusions

Transmission spectra of commercial-grade BS, FQ, PMMA, and PC samples
irradiated with $\gamma$-rays up to 100 kGy are presented, and the degradation
mechanism of each element is discussed. The obtained results indicate that, in
terms of retained transparency upon irradiation, FQ is the most appropriate
material for windows in rad-hard LED luminaires, while BS should be avoided;
moreover, PMMA should be preferred over PC for secondary optics components. In
addition, a Si diode bridge and a SiC JBS diode are tested using 24 GeV/_c_
protons, and radiation effects on both devices are discussed; the SiC JBS
diodes measured were more than one order of magnitude more resistant than Si
diodes, and therefore should be employed in future power supplies for rad-hard
LED lights. In particular, the obtained results show that SiC JBS diodes and
samples of FQ and PMMA exhibit no or limited degradation after radiation
exposure levels equivalent to 10 years on the tunnel wall of a typical section
of CERN accelerator complex, thus meeting the requirements on the expected
lifetime of the rad-hard LED luminaires for CERN tunnels.

## Acknowledgment

The authors gratefully acknowledge Giuseppe Pezzullo (CERN) and Federico
Ravotti (CERN) for performing sample irradiation and assistance at IRRAD
facility, Elisa Guillermain (CERN) and Sophie Rouif (Ionisos) for managing the
$\gamma$-ray irradiation at Ionisos, and Thomas Schneider (CERN) for providing
the spectrophotometer and assistance at the Thin Film and Glass Laboratory
(CERN).

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# S3CNet: A Sparse Semantic Scene Completion Network for LiDAR Point Clouds

Ran Cheng1∗, Christopher Agia2∗, Yuan Ren1, Xinhai Li1, Liu Bingbing1 ∗
Indicates equal contribution.1 are with Huawei, Noah Ark Lab. (e-mail:
ran.cheng1, yuan.ren3, xinhai.li, liu.bingbing@huawei.com).2 is with
University of Toronto, Toronto. (e-mail: christopher.agia@mail.utoronto.ca).

###### Abstract

With the increasing reliance of self-driving and similar robotic systems on
robust 3D vision, the processing of LiDAR scans with deep convolutional neural
networks has become a trend in academia and industry alike. Prior attempts on
the challenging Semantic Scene Completion task - which entails the inference
of dense 3D structure and associated semantic labels from ”sparse”
representations - have been, to a degree, successful in small indoor scenes
when provided with dense point clouds or dense depth maps often fused with
semantic segmentation maps from RGB images. However, the performance of these
systems drop drastically when applied to large outdoor scenes characterized by
dynamic and exponentially sparser conditions. Likewise, processing of the
entire sparse volume becomes infeasible due to memory limitations and
workarounds introduce computational inefficiency as practitioners are forced
to divide the overall volume into multiple equal segments and infer on each
individually, rendering real-time performance impossible. In this work, we
formulate a method that subsumes the sparsity of large-scale environments and
present S3CNet, a sparse convolution based neural network that predicts the
semantically completed scene from a single, unified LiDAR point cloud. We show
that our proposed method outperforms all counterparts on the 3D task,
achieving state-of-the art results on the SemanticKITTI benchmark [1].
Furthermore, we propose a 2D variant of S3CNet with a multi-view fusion
strategy to complement our 3D network, providing robustness to occlusions and
extreme sparsity in distant regions. We conduct experiments for the 2D
semantic scene completion task and compare the results of our sparse 2D
network against several leading LiDAR segmentation models adapted for bird’s
eye view segmentation on two open-source datasets.

> Keywords: Sparse Convolution, Semantic Scene Completion, Autonomous Driving,
> Deep Learning

##  1 Introduction

Scene understanding is a challenging component of the autonomous driving
problem, and is considered by many as a foundational building block of a
complete self-driving system. The construction of maps, the process of
locating static and dynamic objects, and the response to the environment
during self-driving are inseparable from the agent’s understanding of the 3D
scene. In practice, scene understanding is typically approached by semantic
segmentation. When working with sparse LiDAR scans, scene understanding is not
only reflected in the semantic segmentation of the 3D point cloud but also
includes the prediction and completion of certain regions, that is, semantic
scene completion. Scene completion is the premise of 3D semantic map
construction and is a hot topic in current research. However, as semantic
segmentation based on 2D image content has reached very mature levels, methods
that infer complete structure and semantics from 3D point cloud scenes, which
is of significant import to robust perception and autonomous driving, are only
at preliminary development and exploration stages. Limited by the sparsity of
point cloud data and the lack of features, it becomes very difficult to
extract useful semantic information in 3D scenes. Therefore, the understanding
of large scale scenes based on 3D point clouds has become an industry
endeavor.

(a)
Figure 1: Semantic Scene Completion on SemanticKITTI Dataset.

Semantic segmentation and scene completion of 3D point clouds are usually
studied separately [2, 3], but with the emergence of large-scale datasets such
as ScanNet [4] and SemanticKITTI [1], researchers have discovered a deep
intertwining of an object’s semantics with its underlying geometry, and since,
have begun exploiting this with the joint learning of semantic segmentation
and scene completion to boost model performance [5]. For instance, speculating
that an object occluded by vehicles and surrounded by leaves is a trunk
simplifies the task of inferring it’s shape. Conversely, inferring the shape
of a pole-like object forms a prior on it’s semantic class being a trunk
rather than a wall. While previous semantic scene completion methods built on
dense 2D or 3D convolutional layers have done well in small-scale indoor
environments, they have struggled to maintain their accuracy and efficiency in
outdoor environments for several reasons. For one, dense 2D convolutional
methods that thrived in the feature rich 2D image space are no longer
sufficient when tackling large and sparse LiDAR scans that contain far fewer
geometric and semantic descriptors. Furthermore, the dense 3D convolution
becomes extremely wasteful in terms of computation and memory since the
majority of the 3D volume of interest is in fact empty. Thereby, our main
contributions are listed as the following: (a) a sparse tensor based neural
network architecture that efficiently learns features from sparse 3D point
cloud data and jointly solves the coupled scene completion and semantic
segmentation problem; (b) a novel geometric-aware 3D tensor segmentation loss;
(c) a multi-view fusion and semantic post-processing strategy addressing the
challenges of distant or occluded regions and small-sized objects. Given a
single sparse point cloud frame, our model predicts a dense 3D occupancy
cuboid with semantic labels assigned to each voxel cell (as shown in Fig. 1),
generating rich information of the 3D environment that is not contained in the
original input such as gaps between LiDAR scans, occluded regions and future
scenes.

In order to effectively complete occluded voxel regions from LiDAR scans, we
focus on exploiting the geometrical relationship of the 3D points both locally
and globally. In this work, we utilize point-wise normal vectors as a
geometrical feature encoding to guide our model in filling the gaps according
to the object’s local surface convexity. We also leverage a LiDAR-based
flipped Truncated Signed Distance Function (fTSDF [5]) computed from a
spherical range image as a spatial encoding to differentiate free, occupied
and occluded space of a scene. As for future scenes, because these regions are
far from the vehicle and are primarily road or other forms of terrain, we
propose a 2D variant of the sparse semantic scene completion network to
support the construction of the 3D scene via multi-view fusion with Bird’s Eye
View (BEV) semantic map predictions. To tackle sparsity, we leveraged the
Minkowski Engine [6], an auto-differentiation library for sparse tensors to
build our 2D and 3D semantic scene completion network. We have also adopted a
combined geometric inspired semantic segmentation loss to improve the accuracy
of semantic label predictions. Since our network is trained in a complex real-
world autonomous driving dataset with 20 classes of dynamic and static
objects, and the input data is simply a voxelized LiDAR point cloud appended
with geometrical and spatial feature encodings, our model can be deployed on-
the-go with various LiDAR sensors. We demonstrate this by applying our method
to unseen real-world voxel data, which yields reasonable qualitative results.
Our experiments show that our model outperforms all baseline methods by a
large margin, with exceptional performance in the prediction of small, under-
represented class categories such as bicycles, pedestrians, traffic signs and
more.

##  2 Related Works

We review the related works across four major areas: volume reconstruction,
point cloud segmentation, semantic scene completion, and multi-view fusion.

Volume Reconstruction. There are several approaches to inferring complete
volumetric occupancy of shapes and scenes from partial or sparse geometric
data. Efficient methods based on object symmetry [7, 8] and plane fitting [9]
apply for small non-complex completion tasks. In larger scenes with irregular
objects, the former are supplanted by methods that fit 3D mesh models to
object instances based on the local scene geometry [2, 10, 11, 12]. Yet, a
lack of diversity within the 3D model library often leads to incomplete
reconstruction, and expanding the library slows down retrieval. Attempts to
simplify the process to 3D bounding box fitting neglects the local geometry of
objects [13, 14]. Other studies process grid-octree data with CNNs to predict
high resolution outputs [15, 16], but are tailored to reconstructing
individual objects rather than entire scenes.

(a)
Figure 2: Full system pipeline.

Point Cloud Segmentation. A variety of methods segment range images
constructed by spherical projection of a point cloud with deep networks, and
back-project the predicted semantic classes onto the corresponding points in
3D space [17, 18, 19]. The small sized range image tensor that these methods
operate on leads to unparalleled speed, but $\sim 25\%$ of the original point
cloud is unrecoverable after the initial spherical projection. Alternative
approaches project point clouds onto a bird’s eye view perspective, computing
pillar features (voxels) from 3D points to construct a BEV map and process it
with deep CNNs [20, 21]. Another emergent stream aims to segment point clouds
by hierarchically extracting features from the 3D points directly to capture
local and global context of the scan [22, 23]. While these methods have had
reasonable success, they are typically an order of magnitude slower than their
non-direct counterparts.

Semantic Scene Completion. Most mainstream methods are built on small indoor
scenes with dense depth maps or dense point clouds, which effectively reduces
the impact of sparseness on scene completion. The representative methods are
SSCNet [5] and ScanNet [4]. Their approach transforms dense depth maps into a
volumetric TSDF signal and passes it through a dense 3D network. Extensions
are made in TS3D [24] by incorporating semantic segmentation from RGB images
into the construction of the input TSDF volume. In outdoor scenes, these
methods are limited by the vast increase in sparsity and their network
architectures that predict at reduced voxel resolutions. Improvements are made
in SATNet [25], which maintains high output resolution with a series of
dilated convolutional modules (TNet) to capture global context, and propose
configurations for early and late fusion of semantic information. Behley et
al. [1] produced a state-of-the-art system by integrating semantic priors from
color images and LiDAR segmentation with the TSDF volume, and adopting a TNet
network backbone. However, their memory intensive design requires users to
infer on 6 equal parts of the input before fusing the semantic completed
partial scenes, deterring potential real-time usage. Furthermore, the
dependence on color images introduces instability in the overall system under
low-light and poor weather conditions. Therefore, the design of a semantic
scene completion method based solely on 3D point cloud data is motivated by
the need for faster inference speeds, a smaller memory footprint, and
robustness to extreme conditions.

Multi-view Fusion. Fusing semantic and geometric features across various view-
points and dimensions has been explored for a variety of tasks. Several of the
aforementioned semantic scene completion methods demonstrate the lifting of
image-space semantic labels into 3D space [24, 25]. Similarly, Hane et al.
[26] proposed a joint optimization strategy for semantic image segmentation
and scene reconstruction, using the completed 3D scene as a geometric prior on
the corresponding image pixels to enforce spatially consistent segmentations.
Extensions were made to support city-scale reconstruction at reasonable memory
costs with an adaptive multi-resolution model [27]. Dai and Nießner [28]
designed an 2D-3D network that fuses semantic features from RGB-D images with
a differential backprojection layer, achieving multiple view-point
reconstruction in indoor settings. Meanwhile, SLAM-based approaches [29] aim
to produce temporally consistent reconstructions by tracking motion states
over sequential frames and mapping predicted semantics into 3D space.

##  3 Method

We describe our methods for LiDAR-based semantic scene completion in large
outdoor driving scenes. After a brief system overview, we present our unique
procedure for computing key spatial features from an input LiDAR scan, the
detailed design of our networks, fusion module and refinement module, and a
novel loss function incorporating a geometric-aware 3D segmentation loss.

###  3.1 System Pipeline

The entire system pipeline is shown in Fig. 2. From a single LiDAR scan, we
construct two sparse tensors that encapsulate the scene into memory efficient
2D and 3D representations. Each tensor is passed through their corresponding
semantic scene completion network, 2D S3CNet or 3D S3CNet, to semantically
complete the scene in the respective dimension. We propose a dynamic voxel
fusion method to further densify the reconstructed scene with the predicted 2D
semantic BEV map (detailed discussion in Section 3.3). This offsets the
significant memory demands on the 3D network - exponential sparsity growth in
3D space makes it difficult to complete classes at range. Using a sparse
tensor spatial propagation network [30], we refine the semantic labels in
noisy regions of the fused 2D-3D predictions.

###  3.2 Spatial Feature Engineering

Figure 3: Sparse 3D tensor features.

Sparse 2D Feature. We define the sparse 2D tensor as the set of non-empty
pillars approximating the point distribution along the $x$-$y$ plane (BEV).
Each pillar, $p_{i,j}$, is a 7-dimensional vector encoding the mean, minimum,
and maximum heights and intensities of points within the voxel, and the point
density; all values are normalized.

Sparse 3D Feature. The sparsity of the raw point cloud makes it difficult to
extract spatial features, and thus, we transform it into a range image via
spherical projection. As the quality of extracted spatial features are
sensitive to noise, we retrieve a smooth range image by performing dynamic
depth completion using the dilation method of Ku et al. [31]. This enables
robust extraction of a 3-dimensional normal surface feature for each pixel
that is reversely assigned to the points in 3D space. Further, we maintain a
memory efficient sparse 3D tensor by modifying the sign-flipped TSDF approach
of Song et al. [5], and compute TSDF values from the smooth range image,
storing only the coordinates within the truncation range of existing LiDAR
points. All other features for TSDF generated coordinates are zero-padded.

###  3.3 Network Architecture

We adopt the Minkowski Engine [6] as our sparse tensor auto-differentiation
framework to build the entire system. A sparse tensor can be defined as a
hash-table of coordinates and their corresponding features:
$\mathbf{x}=[\mathbf{C}_{n\times d},\mathbf{F}_{n\times m}]$. Here, $n$ are
the number of non-empty voxels, $d$ and $m$ are the dimension of coordinates
and features, respectively. The sparse convolutional layer is thus:

$\mathbf{x}_{\mathbf{u}}=\sum_{\mathbf{i}\in\mathcal{N}^{D}(u\mathbf{u},K,\mathcal{C}^{in})}{\mathbf{W_{i}}\mathbf{x_{\mathbf{u+i}}}}\textrm{
for }\mathbf{u}\in\mathcal{C}^{out}$ (1)

Where $K$ is the kernel size and
$\mathcal{N}^{D}(\mathbf{u},K,\mathcal{C}^{in})$ are the set of offsets that
are at most $\lceil\frac{1}{2}(K-1)\rceil$ away from $\mathbf{u}$, the current
coordinate. Unlike the conventional convolution, this generalized convolution
(introduced by Choy et al. [6] _et al_) suits generic input and output
coordinates, and arbitrary kernel shapes. It allows extending a sparse tensor
network to extremely high-dimensional spaces and dynamically generating
coordinates for generative tasks. The arbitrary input shape and multi-
dimensional support enables us deploy the same network layout in different
dimensional spaces. Hence, our 2D and 3D networks share the same set of
network components (built off sparse convolution, transposed convolution and
pooling layers) with differing coordinate dimensions.

(a)

(b)

(c)

Figure 4: S3CNet network architecture. Example target masks are visualized as
target$\\_$s2-16.

After extracting the features as described in Section 3.2, we create the
sparse 3D tensor by voxelizing the point cloud and extracting the coordinates
and features of non-empty or TSDF generated points. If multiple points occupy
the same voxel, their features are averaged. The voxel resolution is 0.2m in
each dimension; this applies to the sparse 2D tensor as well. The spatial
extent of our predictions in the $x$-$y$-$z$ directions are [0m, 51.2m],
[-25.6m, 25.6m], [-2m, 4.4m], respectively. Discretizing the volume yields a
[256, 256, 32] sized tensor, upon which our sparse 3D network will predict the
set of occupied coordinates and a probability distribution over the 20
possible semantic categories.

(a)

(b)

Figure 5: Diagrams that illustrate how to calculate the local geometric
anisotropy and gradient for a given prediction label voxel grid.

As illustrated in Fig. 4, our 3D network is assembled from four primary
building blocks: encode, decode, dilation, and spatial propagation. We
implement a squeeze re-weight (SR) [18] layer to model inter-channel
dependencies and improve generalization. Each encoder block contains a context
aggregation module (CAM) [17] which captures context in a large receptive
field, improving robustness to dropout noise. We adapt these modules for
sparse tensor support, and observe improvements to both the segmentation and
completion tasks. The decode blocks utilize sparse transposed convolutions
capable of generating new coordinates with the outer product of the weight
kernel and the input coordinates. As new coordinates in 3D are generated by
the cube of the kernel size, we preserve memory with pruning modules that
remove redundant coordinates throughout scene completion - training
supervision is provided by ground truth filter masks (Fig. 4,
target$\\_$s2-16). The dilation block features a sparse atrous spatial pyramid
pooling (ASPP) module to trade-off accurate localization (small field-of-view)
with context assimilation (large field-of-view), our dilation rates are
[2,3,4]. The spatial propagation block contains two parts, 3D spatial
propagation module and guidance convolution network. The guidance network
produces affinity matrices used to guide the spatial propagation network to
deform the sparse tensor into a desired 3D shape.

Loss function. When training our 2D network, we accomplish healthy BEV
completion and balanced learning of under-represented class categories by
combining pixel-wise focal loss, weighted cross entropy loss. Per-voxel binary
cross entropy loss is used to train the pruning modules.

$\mathcal{L}_{2D}(p,y)=-(\alpha\sum_{c\in
C}^{C}w_{c}y_{c}log(p_{c})+\beta\sum_{c\in
C}^{C}y_{c}(1-p_{c})^{\gamma}log(p_{c}))+\omega\mathcal{L}_{\mathbf{BCE}}(p,y)$
(2)

Here, $p$ is the predicted label and $y$ is the ground truth label. The
weighting factors $\alpha$, $\beta$, and $\omega$ are empirically set to 0.5,
0.5, and 1, respectively. We define a novel loss function to train our 3D
network. It consists of a completion term (voxelized binary cross entropy) and
a geometric-aware 3D tensor segmentation loss. The two terms are balanced by
an $\lambda$ constant empirically set to 0.35.

$\mathcal{L}_{3D}(p,y)=\lambda\mathcal{L}_{\mathbf{completion}}(p,y)+(1-\lambda)\mathcal{L}_{\mathbf{GA}}(p,y)$
(3)

The completion loss is expressed below, where $\mathcal{L}_{\mathbf{BCE}}$ is
the binary cross entropy loss over volumetric occupancy. Note that this is
applied to train the pruning modules at various scale spaces.

$\mathcal{L}_{\mathbf{completion}}(p,y)=\sum_{i,j,k}{\mathcal{L}_{\mathbf{BCE}}(p_{ijk},y_{ijk})}$
(4)

Below is the geometric-aware 3D tensor segmentation loss computed over the
final output tensor.

$\mathcal{L}_{GA}(p,y)=-\frac{1}{N}\sum_{i,j,k}{\sum_{c=1}^{C}{(\xi+\eta
M_{LGA})y_{ijk,c}log(p_{ijk,c})}}$ (5)

Figure 6: Birds eye view 2D results compared with 3D predictions.

The terms $M_{LGA}$, $\xi$ and $\eta$ describe signals computed from the same
local cubic neighborhood of a given coordinate (As shown in Fig. 5). The Local
Geometric Anisotropy defined by Li et al. [32],
$M_{LGA}=\sum_{i=1}^{K}{(c_{p}\oplus c_{qi})}$, is a discrete smoothness
promoting signal that penalizes the prediction of many classes within a local
neighborhood. Although it ensures locally consistent predictions in
homogeneous regions (i.e. road center, middle of a wall), it may incorrectly
divert the model from inferring boundaries between separate objects. We thus
introduce $\eta$, which accounts for the local arrangement of classes based on
the volumetric gradient, and downscales $M_{LGA}$ when the neighborhood
contains structured predictions of different classes. To smoothen-out the loss
manifold, we include a continuous entropy signal $\xi=-\sum_{c\in
C^{\prime}}{P(c)log(P(c))}$, where $P(c)$ is the distribution of class $c$
amongst all classes $C^{\prime}$ in the local neighborhood.

Eq. (5) thus decomposes into two multiplicative factors with the cross-entropy
loss which explicitly models the relationship between a predicted voxel and
it’s local neighborhood. Intuitively, the smooth local entropy term, $\xi$,
down-scales the loss in easily classified homogeneous regions, enabling the
network to attend to non-uniform regions (e.g. class boundaries) as learning
progresses. However, a measure of non-uniformity alone is insufficient in that
non-uniform regions should be more heavily penalized if the predicted
neighborhood lacks structure. This motivates the inclusion of $\eta M_{LGA}$
which also considers spacial arrangement of classes and down-scales the loss
in structured cubic regions. In combination, we acquire a smooth loss manifold
when the local prediction is close to the ground truth as well as uncluttered,
with sharp increases when the local cubic neighbourhood is noisy and far off
from the ground truth. This speeds up convergence while reducing the chance of
stabilizing in local optimums.

Multi-view Fusion and Spatial Propagation.

The task of 2D semantic scene completion is far less complex than it’s 3D
counterpart because it does not account for the local structure of objects
along the $z$ dimension, relegating the task to semantic filling in the bird’s
eye view plane. This allows our sparse 2D network to more easily understand
the global distribution pattern of semantic classes even at far distances,
yielding accurate predictions of roads, terrain, and side walks. When
confronted with heavy noise or occlusions, such as Fig. 6, our 3D model may
lack the confidence to complete certain regions. To remedy this, we express
the predicted 3D tensor as a stack of BEV images, and attempt to fill in each
empty voxel with the corresponding prediction in the 2D network. The lifting
algorithm operates as follows: (1) split the 3D volume into layers along the
z-dimension (32 layers in this application); (2) locate the slice with the
maximum occurrence of the desired class; (3) fill each voxel with the
associated 2D pixel prediction if there exists such a class in the voxel’s
$n\times n$ ($n=3$) neighborhood, otherwise attempt to fill in the above voxel
until the highest layer is exceeded; (4) repeat for all classes. Hence, by
lifting the 2D voxel-wise semantic completion into 3D space, we improve both
completion and segmentation metrics of the 3D task at minimal cost. To
mitigate any additional post-fusion noise, we apply a spatial propagation
network that refines the segmentation results in 3D space.

##  4 Experiments

Figure 7: Qualitative results of predictions on SemanticKITTI test sequences.
Best viewed in color.

We evaluate our system on the SemanticKITTI dataset [1], which contains 8,728
voxelized LiDAR scans and the corresponding ground truth labels. We use the
public train/val/test split defined in the SemanticKITTI API, and provide
qualitative visualizations of our predictions. For evaluation metrics, we
follow [5] using mean IoU over positive classes and binary per-voxel
completion IoU. Experiments are deployed on Nvidia GP100 GPUs (16GB Graphic
RAM); we use two GPUs per model and train for 50 epochs each. The average
inference time for 3D S3CNet is 0.5s (frame with 40,000$\pm$500 points), and
training requires around 120 hours. For 2D S3CNet, the average inference time
is 0.05s resulting in 50 training hours. The training scheme for the best
performing 2D and 3D models are: (3D) Adam optimizer, 0.0025 learning rate,
0.0005 weight decay, and (2D) SGD optimizer, 0.001 learning rate, and 0.0005
weight decay. Both experiments also incorporate an exponential learning rate
scheduler with a decay rate of 0.9 every 10 epochs.

Figure 8: 2D qualitative results.

Qualitative Results. Fig. 9 depicts the inferences of our system on three
SemanticKITTI test set samples and the corresponding RGB images. We notice
that our method correctly captures most classes with exceptional detail and
consistency, including small object categories like people and traffic signs
in challenging scenes. The middle column highlights an open intersection scene
with heavy front-facing occlusion, complicating the task of inferring distant
mid-intersection vehicles. For 2D S3CNet, we demonstrate the prediction and
ground truth BEV map in static scenes and dynamic scenes with moving vehicles
(as shown in Fig. 8).

Approach mIoU completion road sidewalk parking other-ground building car truck
bicycle motorcycle other-vehicle vegetation trunk terrain person bicyclist
motorcyclist fence pole traffic-sign Ours 0.295 (1) 0.45 (5) 0.42 (6) 0.22 (6)
0.17 (6) 0.07 (4) 0.52 (1) 0.31 (3) 0.06 (2) 0.415 (1) 0.45 (1) 0.16 (1) 0.39
(5) 0.34 (1) 0.21 (6) 0.45 (1) 0.35 (1) 0.16 (1) 0.31 (1) 0.31 (1) 0.24 (1)
JS3C-Net 0.238 (2) 0.56 (1) 0.64 (1) 0.39 (1) 0.34 (1) 0.14 (1) 0.394 (2) 0.33
(1) 0.072 (1) 0.14 (2) 0.08 (2) 0.12 (2) 0.43 (1) 0.19 (5) 0.40 (1) 0.08 (2)
0.05 (2) 0.00 (2) 0.30 (2) 0.18 (3) 0.15 (3) gjt 0.21 (3) 0.54 (2) 0.61 (4)
0.36 (2) 0.32 (2) 0.06 (5) 0.38 (3) 0.32 (2) 0.05 (3) 0.02 (3) 0.03 (4) 0.07
(4) 0.39 (4) 0.18 (6) 0.34 (3) 0.04 (3) 0.01 (4) 0.00 (3) 0.27 (3) 0.15 (6)
0.12 (4) ifelse 0.183 (4) 0.51 (3) 0.63 (2) 0.31 (3) 0.27 (3) 0.14 (2) 0.29
(6) 0.29 (5) 0.039 (6) 0.00 (5) 0.00 (5) 0.00 (5) 0.40 (2) 0.21 (3) 0.34 (2)
0.00 (5) 0.00 (5) 0.00 (5) 0.25 (4) 0.19 (2) 0.06 (5) jbehley [1] 0.177 (5)
0.50 (4) 0.62 (3) 0.31 (4) 0.23 (5) 0.06 (6) 0.34 (4) 0.30 (4) 0.04 (5) 0.00
(5) 0.00 (5) 0.00 (6) 0.40 (3) 0.21 (2) 0.33 (4) 0.00 (5) 0.00 (5) 0.00 (5)
0.24 (5) 0.16 (4) 0.06 (6) yanx27 0.17 (6) 0.41 (6) 0.43 (5) 0.28 (5) 0.26 (4)
0.10 (3) 0.29 (5) 0.26 (6) 0.05 (4) 0.00 (4) 0.04 (3) 0.09 (3) 0.35 (6) 0.20
(4) 0.28 (5) 0.02 (4) 0.07 (3) 0.00 (4) 0.23 (6) 0.16 (5) 0.16 (2) Source:
https://competitions.codalab.org/competitions/22037

Table 1: SemanticKITTI Test Set Benchmark. ($*$) is the prediction IoU rank.

Model mIoU SK NS HDUNet [20] 0.1486 0.2777 PointSeg [18] 0.1204 0.2317 SSv2
(w/o CRF) [17] 0.1294 0.1718 DBLiDARNet [19] 0.1682 0.2382 SalsaNext [33]
0.1780 0.2715 Ours 0.27003 0.3032

Table 2: 2D S3CNet quantitative results and comparison to state-of-the-art on
SemanticKITTI validation set (SK) and nuScenes test set (NS). [34])

Quantitative results. Our primary results are shown in Table. 1, where we
compare our overall system to several state-of-the-art methods in the
SemanticKITTI test set benchmark. Note the approaches without citations are
non-published works. At the time of writing our proposed S3CNet considerably
outperforms all others, achieving a mean IoU score of 29.5$\%$ (a +23.9$\%$
improvement on the previous leading method) and a +66.6$\%$ over the baseline
method [1]. For 2D S3CNet, we conduct extra experiments on the nuScenes
dataset [34]. Ground truth 2D semantic scenes are created from object bounding
boxes and cropped HD map data, and aligned with LiDAR frames at an identical
spatial extent and voxel resolution to SemanticKITTI. We produced 1,166,187
frames of valid LiDAR scan and semantic scene label pairs and we split the
train, validation, test into a 14:3:3 ratio. The total training time of our 2D
S3CNet for 50 epochs on the nuScene dataset is $\sim$350 hours. Table. 2 shows
that our 2D S3CNet outmatches several well-known LiDAR segmentation baselines
(adapted for BEV predictions) on the segmentation component of the SSC task,
with comparable results on the completion component for both the SemanticKITTI
and nuScenes datasets.

Ablation study. We investigate the individual contribution of all components
in our system. As shown in Table. 3, we conduct various experiments on the 2D
and 3D SSC task by modifying or removing core components of the system and
track the resulting effect on mean IoU and completion IoU scores on the
SemanticKITTI validation set. For both the 2D and 3D networks, we observe a
mean IoU drop after removing the CAM and SR modules. Since the decode blocks
are mainly responsible for completion, removing SR modules results in a more
severe drop in completion IoU compared to CAM which are only present in the
encoder. Eliminating spatial features decreases overall performance by a large
amount; the system losses geometric priors that guide completion and spatial
priors that distinguish free from occluded space. Adopting Lovasz-softmax
achieves the highest mean IoU increase, since it directly optimizes for the
mean IoU metric (Jaccard index). In our experiments, focal loss combined with
binary cross entropy loss provides no performative advantage over the baseline
weighted cross entropy loss for both the 2D and 3D networks. Post-processing
modules like Multi-view Fusion and Spatial Propagation Network demonstrate
very high contribution to the final results - without MVF the system
performance degrades to that of the focal loss baseline. A key distinction
between MVF and SPN is the comparative impact of MVF on completion IoU to the
SPN on segmentation mean IoU, respectively.

Dimension Model Features Losses MVF SPN Data Aug. mIoU (val) Completion IoU
(val) Normal TSDF $\mathcal{L}_{GA}$ (Ours) WeightedCE focal Lovasz BinaryCE
3D Full Model ✓ ✓ ✓ ✓ ✓ ✓ ✓ 0.3308 0.5712 2D ✓ ✓ ✓ ✓ - - ✓ 0.2789 0.7032 3D
w/o CAM ✓ ✓ ✓ ✓ ✓ ✓ ✓ 0.2974 0.5833 2D ✓ ✓ ✓ ✓ - - ✓ 0.2803 0.6880 3D w/o SR ✓
✓ ✓ ✓ ✓ ✓ ✓ 0.3005 0.5455 2D ✓ ✓ ✓ ✓ - - ✓ 0.2604 0.6097 3D w/o Feature ✓ ✓ ✓
✓ ✓ 0.2795 0.4345 2D ✓ ✓ - - ✓ 0.2518 0.6597 3D Lovasz-softmax ✓ ✓ ✓ ✓ ✓ ✓ ✓
0.3012 0.5934 2D ✓ ✓ ✓ ✓ - - ✓ 0.2633 0.6089 3D Focal Loss (baseline) ✓ ✓ ✓ ✓
✓ ✓ ✓ 0.2853 0.5319 2D ✓ ✓ ✓ ✓ - - ✓ 0.2403 0.7136 3D w/o MVF ✓ ✓ ✓ ✓ ✓ ✓
0.2780 0.4430 2D - - - - - - - - - - - - 3D w/o SPN ✓ ✓ ✓ ✓ ✓ ✓ 0.2493 0.4902
2D ✓ ✓ ✓ ✓ - - ✓ 0.2303 0.6736

Table 3: Ablation study on 2D and 3D S3CNet models and core system components.

Data augmentation. To increase model robustness, we integrate a series of data
augmentation techniques into the training of our 2D and 3D networks. For both
2D and 3D tasks we apply uniform random cropping and dropout to the LiDAR
point cloud, as well as uniform random translation of $\pm$0.1m in all three
dimensions. On the 2D datasets, random rotations of $\pm 45^{\circ}$ are
applied only on the yaw angle, however, we apply random rotation of $\pm
10^{\circ}$ on any two of the three Euler angles (roll, pitch, yaw) at a time
when training the 3D model.

##  5 Conclusion

In this paper, we presented a Sparse Semantic Scene Completion Network,
S3CNet, capable of efficiently reconstructing large outdoor scenes and
predicting semantic voxel-wise labels from a single LiDAR scan. To complement
S3CNet, we designed a novel geometric-aware sparse tensor segmentation loss
that promotes class-consistent predictions in homogeneous regions while
encouraging locally structured multi-class segmentations along object
boundaries. In combination with Multi-View Fusion and Spatial Propagation
post-processing modules, our method achieves state-of-the-art results on the
SemanticKITTI test set benchmark by a large margin. We also adapt several
leading LiDAR segmentation networks as baselines for 2D semantic scene
completion, and demonstrate that the 2D variant of S3CNet outperforms these
baselines on two large-scale datasets. Furthermore, we include a detailed
ablation study highlighting the contribution of each individual component to
the overall system. Future work holds the extension of our sparse tensor
method to real-time speeds with improved performance across the board,
additional investigation on useful spatial feature encodings, and a learning-
based multi-view fusion technique to enable end-to-end learning.

#### Acknowledgments

We would like to thank Professor John K. Tsotsos for reviewing this work, as
well as the anonymous reviewers for their valuable suggestions.

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##  6 Appendix A: Extra Qualitative Results

We present additional qualitative results on the SemanticKITTI training set.
As we can see in Fig. 9, our model well captures both completion and semantic
segmentation characteristics of different scenes. Because a single ground
truth label was constructed from LiDAR scans across several time steps (in
order to densify the scene), dynamic objects were filtered out to avoid
labelling noise. For instance, in the bottom-right most sample, while the bus
was filtered out of the ground-truth scene, our well engineered features
enabled our model to detect the object with the correct class label. Another
example of this is visible in the top-right most sample, where our model
detects a moving bicyclist in front of a vehicle. These object labels are
learned from static scenes, but are successfully inferred in dynamic scenes
which reflects positively on our model’s generalization ability.

Figure 9: Qualitative results of S3CNet in SemanticKITTI dataset.

NuScenes 2D qualitative results: we project the 2D semantic BEV map back to 3D
lidar points according the respective x and y coordinates, and overlay the
semantic point cloud data on the rgb images to show the model’s qualitative
results. As we can see from Fig. 10, our predictions cover most of the road
surface and precisely detects vehicles, pole-like objects and pedestrians.

Figure 10: 2D (BEV map) Semantic scene completion qualitative results in
NuScenes dataset.

##  7 Appendix B: Experiment Configurations

We provide the training scheme for all 2D experiments in Table. 4, which
includes the adapted LiDAR segmentation baselines that have undergone multiple
optimization iterations to achieve the stated results on the SemanticKITTI and
NuScenes datasets. Note that all experiments use identical input data and
augmentation configurations; changes only occur to the model, the loss
function, optimizer, scheduler, and supporting hyperparameters - much of which
are based on the training scheme proposed by the original works.

Model Loss Function Optimizer Scheduler Rates HD-UNet [20] Weighted CE SGD
Step lr = 0.01-0.02 dr = 0.1 m=0.9 wd=0 PointSeg [18] Weighted CE Adagrad Step
lr = 0.005 dr = 0.1 m=0.9 wd=0.0005 SSv2 [17] Focal Loss SGD Step lr = 0.01 dr
= 0.1 m=0.9 wd=0.0001 DBLiDARNet [19] Weighted CE Adam None lr = 0.0001
wd=0.0005 SalsaNext [33] Weighted CE + Lovasz Softmax SGD Exp lr=0.05 dr=0.01
m=0.9 wd=0.0001 Ours Focal Loss + Weighted CE + BCE SGD Exp lr=0.001 dr=0.01
m=0.9 wd=0.0005 lr: learning rate dr: decay rate m: momentum wd: weight decay

Table 4: Training configurations for 2D Semantic Scene Completion models on
SemanticKITTI and NuScenes datasets.

We also list our experiment configurations including the loss function,
hyperparameters, scheduler, and provide the best validation mean IoU for all
3D model experiments (see Table. 5). We implemented a list of state-of-the-art
methods (not currently on the SemanticKITTI benchmark) [5][35][36][32] and
trained them on the SemanticKITTI dataset for 50 epochs. Amongst the
competitors, our model achieved the best mean IoU on the validation split
(sequence 08).

Model Loss Function Optimizer Scheduler Rates Best mIoU SSCNet [5] Weighted CE
SGD Step lr = 0.01 0.182 dr = 0.1 m=0.9 wd=0.0005 AICNet [35] Weighted CE SGD
Step lr = 0.015 0.215 dr = 0.1 m=0.9 wd=0.0005 DDRNet [36] Weighted CE SGD Exp
lr = 0.02 0.193 dr = 0.1 m=0.9 wd=0.0001 PALNet [32] Position Importance Aware
Loss SGD Exp lr = 0.02 0.255 dr = 0.05 m=0.9 wd=0.0001 Ours Geo-Aware Loss +
Binary Cross Entropy SGD Exp lr = 0.025 0.303 dr = 0.1 m=0.9 wd=0.0001 lr:
learning rate dr: decay rate m: momentum wd: weight decay

Table 5: Training configurations for 3D Semantic Scene Completion models in
SemanticKITTI dataset.