the fluorescent filament results in high contrast between the marker and the
rest of the IR image. This allows us to robustly detect and track the objects
in which the markers are embedded.

Our imaging system consists of three key components: (1) a light source for
exciting the marker, (2) an optical filter for isolating marker fluorescence,
and (3) a high-speed infrared camera. We explain the role of these components
in the next sections.

Light source for excitation: Excitation of the NIR fluorescent material within
the 3D printed object is necessary to read the embedded pattern. There are two
considerations that an appropriate light source should meet for this purpose.
First, the light emitted by the light source should be invisible to the user,
i.e., there should be no emission below 700nm. Second, the light should excite
the fluorescent material as much as possible. To achieve this, the light
source should have high power, and its peak wavelength should be as close to
the material’s peak excitation wavelength (i.e., 763nm as explained in Section
7.1.1) as possible.

To satisfy these criteria in practice, we use LEDs that peak at 760nm and
deliver high power. The bottom graph in Figure 7.2b shows the spectrum of the
LED used in our high-speed imaging module (Section 7.2.2), marked red and
labeled "selected light source." We note that the intensity reaches zero at
approximately 800nm.

Optical filter for marker isolation: Through a wavelength-specific filter, we
can enhance the recognition of fluorescent markers by minimizing interference
from other wavelengths.

Due to the natural characteristics of fluorescence, there is an overlap
between the higher wavelength end of the excitation spectrum and the lower
wavelength end of the emission spectrum. This overlap, which is shown in the
top graph of Figure 7.2b, must be eliminated to avoid overwhelming the weaker
emitted fluorescence light with the brighter excitation light, which would
significantly reduce marker contrast.

To separate these signals, we use a longpass filter with a cut-on wavelength
of 830nm, which blocks any wavelength under this value from entering the
camera. As shown in Figure 7.2c, it blocks the excitation light emitted by the
LED (marked red) and the visible environmental light (marked yellow) from
reaching the camera. The only wavelength range that can enter the camera
corresponds to the fluorescence from the marker itself (marked purple).
Therefore, as shown in the top graph of Figure 7.2b, there is no longer an
overlap between the excitation and emission spectra: Our selected light source
cuts the emission at $\sim$810nm, while the filtered fluorescence that enters
the camera starts at $\sim$820nm. This separation allows the high intensity of
the markers in captured images.

High-speed NIR camera: A high-speed camera allows the observation of the
fluorescence emitted from the moving objects.

To capture images at a high frame rate and minimize motion blur, we use a
60-frames-per-second (fps) monochrome camera with a global shutter rather than
a rolling shutter. Rolling shutters, which are commonly used in consumer-grade
cameras, scan the image sensor line by line from top to bottom, resulting in a
time delay between the capture of each row [47]. This can lead to motion blur
in fast-moving scenes, which makes it difficult to detect the BrightMarker
accurately.

In contrast, a global shutter captures the entire image simultaneously,
resulting in no time delay between rows. This is crucial for real-time
tracking where every frame counts. Additionally, a monochrome camera achieves
higher spatial resolution than color cameras, and eliminates the need for a
color filter array, which reduces the amount of light reaching the image
sensor and decreases the camera’s sensitivity to NIR fluorescence [148].

Together, these properties allow us to create a real-time video representation
of the object with high temporal resolution.

###  7.2 Fabricating, Capturing, and Tracking BrightMarkers

In this section, we first describe the workflow in which BrightMarkers are
added to objects for fabrication. We then explain the hardware and software we
use for their detection once printed.

####  7.2.1 Adding Markers for Fabrication

Users start by importing a 3D model into the CAD editor (Blender). Next, the
user specifies how the fluorescent marker(s) should be implanted using our
plugin, which offers a variety of options as shown in Figure 7.4a.

Figure 7.4: Marker embedding process. (a) Our tool allows users to (b)
uniformly distribute markers based on the object geometry.

Determining marker locations: In tracking applications, it is important to pad
the object surface with numerous markers, rather than a single one, to ensure
the object can be tracked independently of the object orientation. The user
can either manually pick exact target points on the object for the marker
embedding, or use an automatic distribution mode. This mode uniformly
distributes points on the object geometry based on a fixed inter-marker
distance or a total number of desired markers. The user can specify whether
the target points should be distributed on the whole object, the areas other
than the base, or a specific area selected by the user.

Determining marker content: The plugin allows users to embed ArUco markers
with the same ID ("Use fixed ArUco ID") or increasing IDs ("Use sequential
ArUco IDs") based on the desired use case. Unique sequential IDs can help
identify an object’s location and orientation. It is also possible to load a
custom marker, such as a QR code or a Micro QR code, by selecting an image
file.

Determining marker depth and dimensions: Next, the user specifies how deep the
marker should be embedded under the object’s surface. For this, the user can
enter a custom value or simply select the desired object color from a
dropdown, after which the plugin automatically retrieves the corresponding
$t_{shell}$ value from Section 7.1.1. Next the user can specify the dimensions
of the marker. The marker thickness is set to the recommended $t_{marker}$
value by default, and the user can specify a desired side length of the
marker.

Finally, the user can specify whether the marker’s bottom edge should be
parallel to a certain plane. This helps users to align codes in the 3D
printing direction, which can lower print time and avoid print failures that
might occur due to frequent nozzle switching. After setting up the parameters,
users can start the embedding by clicking "OK". Once the markers are embedded,
users can export the resulting STL files for 3D printing.

3D printing: The user imports the resulting STL files into the slicer software
of the printer. For the popular slicer
Cura111https://ultimaker.com/software/ultimaker-cura, we recommend disabling
the "combing" parameter to ensure that the printer does not move over the
already extruded pieces of fluorescent filament. This prevents the fluorescent
filament from getting mixed into the outer surface layers and thus becoming
visible.

Implementation: The automatic uniform distribution of the markers is created
by first determining the areas on the model that are large enough and close to
being flat to accommodate a marker of the user’s specified size. This is done
by comparing the angles between the normals of adjacent faces with the method
faces_select_ linked_flat(), grouping the ones with an angle difference
smaller than 0.1 rad, and ensuring that the contained surface area is greater
than that of the marker. This threshold can be adjusted based on the user’s
preference and application if desired. As shown in Figure 7.4b for the
lightsaber model from Section 7.3.3, this gives the subregions on the object
geometry that are not too curvy or sharp.

Next, to uniformly sample points, each subregion is temporarily duplicated,
flattened, and its shape is approximated to a high degree of accuracy in an
array of 1s and 0s created with repeated calls of the function ray_cast(). The
array is analyzed row by row, choosing rows that are a distance of half the
marker side length apart. Points in each row are picked if they are
sufficiently far from each other and the edges of the subregion. Finally,
using the specifications set by the user, a knife projection is used to
project the marker so it has the same curvature as the object at the sampled
point. The projected marker is extruded to form a 3D model, which is exported
separately.

####  7.2.2 Detection Using Imaging Modules

As shown in Figure 7.5a, we built two hardware modules that fulfill the
requirements of the fluorescence imaging principles explained in Section
7.1.2.

Figure 7.5: Hardware modules for tracking BrightMarkers.

Smartphone attachment: The module can be directly plugged into existing phones
using a USB-C connection. It includes eight LEDs that correspond to the
excitation wavelength of our filament. To ensure objects fit in the phone’s
field of view, half of the LEDs have a viewing angle of 60∘ and the other half
120∘ (Stanley FWR1107MS-TR and FWR1108MS-TR, $2.5 each). The attachment weighs
47 grams, and consumes 1.6W during operation. We use it with a OnePlus 8 Pro
that comes with an embedded NIR camera (GalaxyCore
GC5035222https://en.gcoreinc.com/products/index?cid=2&subcid=5), which we
coupled with the appropriate longpass filter for fluorescence (MaxMax, $30).

Stand-alone module: The module is built for high-speed fluorescence imaging
and can be attached to existing AR/VR headsets. It consists of a 60-fps NIR
camera (ArduCam with OV2311333https://www.ovt.com/products/ov2311/, $98)
coupled with the above-mentioned longpass filter, a 10W LED grid (Nagalugu,
$18), and a small battery (generic 9V, $2). A custom PCB utilizes a pulse-
width modulated (PWM) signal to deliver battery power to the LEDs. It uses a
timer IC (LM555) and an n-channel MOSFET to modulate the LED power via a
constant current regulator (LM317), which can be adjusted using a
potentiometer. In total, the module costs $148 and weighs 118g. For
prototyping purposes, we did not include a processing component on the module
itself but connected its camera to an external computer using a cable. In the
future, the module can be augmented with a small microprocessor to run the
compute on-the-go, considering that our approach does not use compute-heavy ML
models.

To be able to attach and detach the stand-alone module to existing AR/VR
headsets, we designed and 3D printed an enclosure that encompasses all the
parts. Figure 7.5b,c shows how the module is mounted on the headsets.

####  7.2.3 Image Processing for Marker Detection

We developed an image processing pipeline to detect the markers in real time.
The process of tracking and decoding the markers is shown in Figure 7.6, and
is explained below.

Figure 7.6: BrightMarker’s image processing pipeline. (a) The objects are
tracked using (b) the outlines in IR capture. (c, d) The localized markers are
decoded using a set of filters.

Tracking the marker: To keep the detection script as lightweight as possible
for fast tracking, we intend to apply a small number of processing steps on
the main image frame when localizing the markers. Thus, the grayscale input
image is simply binarized using Otsu’s thresholding method. The binarization
results in multiple identified contours. We then approximate each contour as a
polygon. The polygons with four sides are our target markers (Figure 7.6b).

This approach keeps track of all markers irrespective of the data encoded in
them (i.e., ID/string). This keeps the detection lightweight if the encoded
data is not relevant to the application, e.g., when all markers are known to
have the same ID.

Decoding the marker: For data-relevant applications, the second part of the
pipeline, marker decoding, is enabled. In this part, we use each marker’s
bounding box from the previous step to crop out a smaller patch from the
original frame (Figure 7.6c). While cropping, we add a small padding around
the marker, i.e., 1/8 of the detected bounding box length. We then resize the
patch to a certain size, i.e., 50px height. We invert the image such that the
markers are dark to follow the ArUco convention. We apply a Gaussian blur to
remove the noise, and apply an adaptive threshold with a block size of 15.
Next, we use the ArUco detection library to decode the marker ID from the
binarized patch.

The patch resizing in this part ensures that the applied block size is
appropriate regardless of marker location and distance. To determine this, we
applied different filtering operations to a test recording consisting of 600
frames, where the marker is moved constantly (distance: 10-80cm). When the
adaptive threshold was applied to an uncropped frame, the markers could be
decoded from only 85.2% of the frames. On the other hand, applying the
threshold after cropping and resizing allows 98.2% of the frames to be
decoded.

If no ArUco marker is found in the patch, the script checks if there is a QR
code or a Data Matrix. For this, we follow the same steps, except for two
differences: Because the codes have more bits than ArUcos, we use a rescaling
height of 100px and an adaptive threshold block size of 9. We then use a
standard library to decode the codes. An example is shown in Figure 7.6d.

If an ArUco or a QR code is found in the patch, the script records its data
and corners in an array.

Tracking decoded markers across frames: We also added a "caching" feature to
our detection pipeline that helps track of marker data across frames, without
having to run the decoding script in every new frame. When caching is enabled,
we attempt to match the newly localized markers to already decoded markers
from the prior frame by comparing their corner coordinates. If a new marker is
found to be similar to a previous marker, we retrieve the marker data from the
previous frame. Otherwise, we run the remaining decoding script. This is
especially useful for QR codes, which traditionally take several frames to be
decoded, whereas the fluorescent outline can already be tracked in all frames.
However, to be conservative, we did not enable this feature during our
evaluations (Section 7.4).

Implementation: We use OpenCV [15] for the implementation of our system and
the Dynamsoft444https://www.dynamsoft.com/ library for the detection of the 2D
barcodes (i.e., QR, Micro QR, and Data Matrix). For rapid prototyping
purposes, we ran the detection script on our laptop during the development of
this project. The processing of a single 640x360px frame takes 3.7 ms on
average, corresponding to a 270fps on a 2020 MacBook Pro with a 2GHz Quad-Core
Intel Core i5 processor. Since our high-speed camera has a limit of 60fps, the
detection in our applications was constrained to this rate, but our software
pipeline can support higher rates as well. In our AR/VR applications, after
running OpenCV’s ArUco pose estimation, we use Unity’s coordinate transform
features to convert the marker’s location to a common local coordinate frame.

###  7.3 Applications

In this section, we show several applications of BrightMarker, in which the
tracking of object locations is an integral part of a process.

####  7.3.1 Rapid Product Tracking

Because BrightMarkers can localize and track embedded objects, it can be used
for industrial or commercial applications in which items need to be processed
in a swift manner. This can be especially useful for product and packaging
logging where, although the external labels may be intentionally or
unintentionally removed, it is still crucial to keep track of the item origin
and other supply chain or inventory-related data.

Tracing products on conveyor belts: In manufacturing and packaging industries,
fluorescent markers could be integrated to enable product or part tracing as
part of assembly lines (Figure 4.1a).

On-the-fly inventory logging: Users can use the smartphone attachment
described in Section 7.2.2 to activate the fluorescence of BrightMarker, and
scan a batch of products by moving the phone to quickly capture all codes.

We note that currently, our approach is more suitable for products that are
already being 3D printed (e.g. shoes [141]), and could be extended to those
that will be printed in the future.

####  7.3.2 Wearables for Tracking Human Motion

One of the applications of BrightMarker is 3D printing custom wearables for
tracking human motion. Figure 7.7a shows rigid and flexible wristbands printed
with embedded markers. Embedding unique markers allows us to digitize the
user’s motion and distinguish between right and left hands (Figure 7.7b). In
our setup, the detection module is mounted above the user’s desk (Figure
7.7c).

Figure 7.7: Wearable for hand tracking. (a) Rigid and flexible bracelet
designs with embedded markers. (b) Fluorescence imaging is used to detect the
unique tracking IDs. (c) The user’s motion is digitized.

Such tracking wearables can allow various use cases unobtrusively, such as
creating digital twins and animations, increasing safety in human-machine
collaborations, posture correction warnings, or device control. Compared to
existing tracking methods, the use of BrightMarker preserves the user’s
privacy since the camera only captures the marker, not the user’s face or
environment. Furthermore, current methods, especially those based on machine
learning, are usually tuned for able-bodied people’s hands. Wearables with
BrightMarker could support applications for people with limb differences or
hand impairments. In our current implementation, we produced the black
wristband using PLA and the red one using ABS. Both wristbands have a small
thickness, thus allowing them to be bent. In the future, more custom wearables
could be printed using more flexible materials such as TPU or by utilizing
FDM-based textile fabrication methods [40, 137].

####  7.3.3 Tangible Interfaces for MR Experiences

BrightMarkers can be embedded into objects to turn them into tangibles with
more precise tracking capabilities for mixed reality (MR) [21]. For instance,
opportunistic tangible interfaces could enhance AR interactions [34], or serve
as haptic placeholders in VR.

Appropriating physical parts as precise AR input tools: Figure 4.1c shows a
loudspeaker that has been unused in an office. The user wants to make use of
the unplugged speaker by transforming it into a passive tangible interface for
controlling the volume and bass of his AR glasses. This allows him to have a
more natural and tangible input method, while touching small buttons on the
glasses can be cumbersome.

BrightMarker has the benefit that the marker objects also include the part’s
identifier, i.e., the top and bottom knobs can be distinguished and assigned
unique functionalities. Further, since the knobs have a uniform color and
shape, it would be difficult to precisely track their rotation without the
embedded BrightMarkers.

Real-life objects as VR haptic props: BrightMarker allows users to make use of
the physical shape of existing real-world objects (e.g., toys, gadgets, sports
gear) as haptic proxies in VR. For example, a "lightsaber" toy could come with
integrated BrightMarkers, so it can be used as a different object in games. As
shown in Figure 7.8, the lightsaber’s hilt is used as a haptic placeholder for
a sword to slice fruits in a game. Another benefit is that the objects are
fully passive, while typical VR game controllers contain infrared LEDs that
need to be powered up.

Figure 7.8: Using a lightsaber as a prop to slice fruits in a game.

####  7.3.4 Privacy-Preserving Night Vision

Traditional security cameras use infrared LEDs to monitor environments at
night. However, these cameras might not be optimal for use in private
environments, such as one’s bedroom, although users may still want an
alternative method to ensure the security of their valuable belongings.
BrightMarker’s imaging system removes all details in the camera stream except
for the marked objects. In Figure 7.9, a box that stores valuables was tagged
with a BrightMarker, and the rest of the object surface was uniformly inlaid
with the fluorescent filament so it can be captured from afar. The camera
detects the shiny outline of the object, it triggers an alarm. While doing
this, it preserves the user’s privacy. This fluorescence-based monitoring
approach could be enabled by modifying existing home security cameras, i.e.,
by attaching the appropriate filter on the camera and an IR source next to it
as described in Section 7.1.2.

Figure 7.9: Privacy-preserving night vision. (a) Regular CCTVs help monitor
important objects but may intrude on users’ privacy. (b) Our detection setup
allows tracking of solely the fluorescent objects.

###  7.4 Evaluation

In this section, we evaluate our system’s performance under a variety of
conditions, including detection distance, illumination, shell color, and
speed.

####  7.4.1 Detection Distance, Excitation Intensity, and Surface Color

Because the embedded filament fluoresces more when exposed to a greater amount
of IR excitation, the maximum distance at which a marker can be detected
depends on the amount of excitation it receives. To evaluate how the IR
intensity affects detection, we conducted an experiment determining the
minimum amount of IR required to track vs. decode markers at varying distances
from the camera. We consider tracking to be when the program finds the bounds
of the BrightMarker; in contrast, decoding occurs when it is also able to
extract the data of the marker (Section 7.2.3).

Procedure: We printed three square BrightMarkers with a 4x4 ArUco pattern of
size 1"x1" (2.54cm x 2.54cm). Each BrightMarker was printed using shell
material of a different color (blue ABS, red ABS, and black IR-PLA). We picked
these to represent the variety in materials and fluorescence strength. We
placed a 220cm tape measure along a table in a dark room (3lux ambient light).
The NIR camera was aligned at the 0cm mark. As a baseline, we also recorded
the maximum distance that a (visible) paper ArUco marker of the same size
could be decoded using solely the off-the-shelf ArUco detection library.

For each trial, we started by placing a BrightMarker 10cm away from the
camera. Then, we moved the excitation light as far away from the marker as
possible until the marker is just able to be tracked. Keeping the light at
this position, we measured the amount of illumination reaching the marker
surface using a Tacklife LM01 digital luxmeter. We then repeated this, but for
the amount of illumination required for decoding rather than tracking. The
minimum illumination needed to track and to decode the BrightMarker was
recorded for each interval of 10cm until 220cm.

Since different shell colors allow for varying levels of fluorescence, we
conducted trials for the different color prints. The chosen colors represent a
wider range of fluorescence, including those which only allow minimal
fluorescence (e.g., blue) and those with substantial fluorescence (e.g., red).
We repeated the procedure three times for each color and plotted the average
results in Figure 7.10.

Figure 7.10: Excitation intensity required with increasing marker distance
from the camera.

Results: The tracking and decoding results are plotted separately for each
color in Figure 7.10. The dashed lines represent the intensity needed to track
the marker at each distance. The solid lines, however, represent the intensity
to decode the marker at each distance.

As a baseline, the dashed green line shows the maximum distance at which a
standard paper marker can be tracked and decoded (i.e., 122cm). We found that
since the NIR capture filters out wavelengths lower than the BrightMarker
emission, markers are able to be tracked at distances far greater than a paper
marker. On the other hand, BrightMarker decoding distances were not expected
to exceed this value, but were expected to be relatively close to it. While
the red and black samples exhibited this behavior, the blue sample exceeded
the expectation and could be decoded just past the point where a paper code
can be detected. We believe this is because the filtered capture allows the
decoding algorithm to ignore the noise that is in the unfiltered capture;
thus, allowing it to decode the marker at a further distance. However, this
does not occur for other colors because unlike blue, which allows lower levels
of fluorescence, the emitted light in the red and black markers slightly bleed
into the shell of the markers.

The plots also indicate a correlation between material color and the intensity
of light required for detection. Overall, lower curves indicate easier
detection than higher curves, since a low curve in this plot means that less
light is needed given a marker distance. The more fluorescence that the shell
lets through, the less excitation is required to detect the marker, e.g., less
light is required for red prints than blue prints. This is expected since the
NIR camera is capturing the emitted light, so the materials that allow for
more emission require less overall excitation. For this reason, we recommend
using shell materials such as red ABS for tracking from far distances using
weaker excitation sources.

####  7.4.2 Detection Rate and Marker Speed

Because the fluorescence of BrightMarkers is imaged using a longpass filter,
our detection setup eliminates non-marker elements from the scene, thus
reducing the chance of undetected frames. We conducted an experiment to
compare how the fluorescence-based detection approach improves the detection
rate compared to regular InfraredTags [29] as the tags are being moved.

Procedure: We printed a red and blue BrightMarker with a 4x4 ArUco pattern
(size: 2.54cm x 2.54cm) to represent high and low fluorescence. We then used
the printer as a CNC tool: We attached each marker separately on the printhead
of an Ultimaker 3, and placed our detection module on the printbed, with its
camera looking upward at the marker. We moved the printhead in an upward
conical helix trajectory to cover the remaining printer volume
(21.6x21.6x130cm), while also staying in the camera’s field of view. We
repeated this for 12 different head speeds (range: 20mm/s - 240mm/s). We
computed the detection rate throughout the trajectory from the resulting
60-fps videos.

Results: Across the 12 speed values, the InfraredTag could be tracked and
decoded in only 60.73% of the frames on average (std=2.26). For BrightMarkers,
the red and blue sample could be tracked in 100% of the frames. The red sample
could be decoded in 99.41% of the frames (std=0.48) and the blue sample 99.83%
(std=0.19). The small standard deviation values across the different speeds
show that the high-speed camera employed in our imaging module successfully
avoids motion blur due to its global shutter (Section 7.1.2).

####  7.4.3 Marker Size

To test what the smallest detectable BrightMarker size is, we printed samples
in 1mm dimension increments (range: 5-10mm) separately for red and blue ABS
surface materials. For both colors, the smallest decodable marker was 6mm
wide. However, the camera had to be 2cm close to the blue marker to decode it,
while the red marker could be decoded from as far as 7cm. This is likely due
to the higher IR absorbance of the blue shell material.

###  7.5 Discussion

In this section, we discuss the limitations of BrightMarker and potential
directions for future research.

####  7.5.1 Concentration of Fluorescent Dye

The concentration of the fluorescent dye used in the filament of BrightMarkers
is an important factor in its detectability.

To test how the dye concentration affects the marker intensity, we obtained
two small test plates from the filament manufacturer, since a high-
concentration filament is not readily available yet. Each plate was doped with
fluorochrome, one with the same concentration as our filament, and another one
with eight times the standard concentration. Given the same level of
excitation, we measured the intensity of the emitted light when the plates are
uncovered. We repeated this after covering both samples with a 1.2mm sheet
made from regular red ABS filament, mimicking BrightMarker’s $t_{shell}$.

In the uncovered comparison, we observed that the highly doped sample resulted
in 33% more IR intensity than the regular sample. In the covered comparison,
which represents the conditions for BrightMarker, the intensity of the highly
doped sample was 20% higher than the regular sample. Thus, to further increase
detection performance, further research and optimization could lead to the
production of filaments with higher fluorochrome concentrations.

####  7.5.2 Mass Production

In the future, the BrightMarker approach may be adapted for mass-market
manufacturing. For instance, instead of 3D printing, plastic overmolding could
be utilized to include fluorescent materials in mass-manufactured products
without any post-processing steps.

####  7.5.3 NIR Power

While the LED component we used in the AR/VR attachment module (Section 7.2.2)
is rated 10W, we did not use it at full power for the headset. The
potentiometer in the PCB allows us to adjust the power from 1.8W up to 3.5W.
At the lowest power, a black IR PLA-covered sample can be tracked from up to
50cm away. At max power, it can be tracked at 90cm away. While, as far as we
know, there are no longitudinal studies yet on NIR and eye safety, commonly
used examples of NIR light include iPhone FaceID, Microsoft Kinect, and Intel
RealSense depth cameras. For instance, RealSense D400’s IR projector [68] can
consume up to 4.25W.

####  7.5.4 Embedding Circular Markers

In this project, we embedded ArUco markers for tracking purposes. Another way
to add trackers would be to use the fluorescent filament insert small circular
markers, similar to OptiTrack’s retro-reflective beads, underneath the object
surface. Such triangulation-based tracking systems are typically deemed more
robust than square marker-based methods. However, since triangulation-based
methods require multiple cameras to be set up, a costly and cumbersome
process, we chose to use ArUco markers for their simplicity which suits our
everyday applications. Interested researchers can use our embedded fabrication
approach for other tracking methods based on their needs and constraints. In
the future, this could also be used to improve detection for more intricate
objects, i.e., those with high-frequency surface details that would lead to
local occlusion of the markers based on the viewing angle.

####  7.5.5 Occlusion

Object occlusion is a typical problem for most tracking methods. Similarly,
BrightMarker could be occluded by the user’s hands and other objects. One way
to overcome this is to couple the optical tracking with magnetic tracking
methods (i.e., hybrid tracking) [130]. A magnetic material could be embedded
in the printed object using magnetic filaments, which would enhance the
tracking when the object is occluded by utilizing magnetic sensors.

###  7.6 Conclusion

We presented BrightMarker, a novel method for embedding and tracking hidden
high-contrast markers using fluorescent 3D printing filaments. Our approach
offers an easy-to-use solution for marker-based tracking without affecting the
object’s look or shape. We showed that BrightMarkers can be embedded in
various object colors, and can be easily localized using a light source and
camera filter that match the fluorescence characteristics of the material. Our
CAD tool allows users to add markers to their 3D models before printing, and
our optical detection hardware can be attached to existing AR/VR headsets for
marker tracking. BrightMarker’s image processing pipeline uses the captured
images to robustly localize the markers.

Our applications demonstrate rapid product tracking, custom-fabricated
wearables, tangible interfaces in AR/VR, and privacy-preserving night vision.
Our evaluation shows that markers of different colors can be detected from
afar and at various object speeds. We believe that our work demonstrates the
potential of using fluorescence as an effective and versatile method for
embedding invisible markers, and we hope it fosters further exploration and
innovation in this area by the HCI and fabrication community.

##  Chapter 8 Discussion

Together, the presented tagging approaches (natural, structural, and internal)
provide effective ways of how to include identifiers and markers in objects
without disrupting their look, feel, and functionality. This is especially
true for natural markers as they, by definition, do not alter any feature of
the object. However, for structural and internal markers, the design needs a
more meticulous thought process so as to not impact these aspects.

While our presented approaches align with the design criteria outlined in
Section 1.1, they also represent a targeted exploration of the broader design
space for embedded markers. The precise dimensions of this design space have
not been the primary focus of this thesis. Nonetheless, future investigations
could extend their scope to provide a more thorough exploration of the
specific dimension axes.

These embedded markers contribute to the bigger vision of enabling users to
more seamlessly identify products, devices, and other items in their
surroundings and access more information related to them rapidly. Further,
these techniques can be used in various industries that inherently rely on
tags, such as retail, packaging, shipping, robotics, and manufacturing.

The following sections explore various aspects and implications of the
embedded markers and the vision of ubiquitous metadata. We explore key topics
related to scaling up for mass production, privacy considerations, the
markers’ use in consumer products, and interaction and personalization in AR.
Through these discussions, we aim to gain deeper insights into the challenges
and potential solutions that arise in the context of digital fabrication,
mixed reality, and ubiquitous metadata.

###  8.1 Scaling Up and Mass Production

The successful implementation of embedded machine-readable markers extends
beyond 3D printing and requires consideration for generalization to more
common and scalable manufacturing methods. In this section, we discuss the
challenges and opportunities associated with extending the use of embedded
tags to diverse manufacturing processes to allow for broader adoption and
mass-scale production.

Scalability is a key aspect to consider when generalizing embedded markers to
more commonly used manufacturing methods. While 3D printing offers flexibility
and customization, its relatively slow production speed makes it less suitable
for large-scale manufacturing. Therefore, it is necessary to explore and adopt
faster and more scalable methods, such as injection molding, casting, and CNC
machining, to meet an increasing demand for tagged objects. To ensure
compatibility with these scalable fabrication methods, the design of embedded
tags should be adaptable and compatible with the specific constraints,
material properties, and production workflows associated with each technique.
This requires careful consideration of factors such as the choice of materials
and geometric design optimizations.

Standardization also plays a vital role in the generalization of embedded
markers to more common manufacturing methods. Developing industry-wide
standards for tag designs and integration processes fosters interoperability
and facilitates seamless adoption across different manufacturers and
industries. Standardization efforts should consider aspects such as tag
specifications and encoding formats. By establishing common practices,
manufacturers could more easily incorporate embedded markers into their
existing production workflows.

Cost considerations are another important factor for scalable manufacturing.
Exploring cost-efficient materials, optimizing fabrication processes, and
identifying opportunities for economies of scale are essential to make
embedded markers economically viable. Finding a balance between cost and
functionality ensures that manufacturers can adopt embedded markers without
significant financial barriers while still reaping the benefits of enhanced
object identification, tracking, and interaction.

###  8.2 Privacy

The widespread adoption of embedded machine-readable markers entails important
privacy implications for end users [18]. In this section, we discuss the
privacy challenges and potential solutions related to embedded markers.

One of the primary privacy concerns is the unauthorized access or misuse of
data encoded in the markers. As embedded markers become more prevalent in
various domains, including retail, robotics, and smart homes, the potential
risks associated with data breaches and unauthorized tracking of individuals
become more significant. It is crucial to implement robust encryption and
authentication mechanisms to protect the integrity and confidentiality of the
information stored in the markers. This includes techniques such as data
anonymization, secure key management, and access control protocols [46, 163]
to prevent unauthorized access and ensure data privacy.

Another privacy consideration is the potential for tracking and profiling of
individuals based on the information collected through embedded markers. In
certain contexts, such as retail or marketing, embedded markers may be used to
track user behavior, preferences, and interactions. To address this concern,
privacy-enhancing technologies and techniques should be utilized, such as data
minimization, privacy-preserving algorithms, and user consent mechanisms [97].
By allowing individuals to have control over their data and providing
transparent information about data collection and usage, privacy risks can be
mitigated.

To address the privacy implications of embedded tags, interdisciplinary
collaborations between researchers, policymakers, and industry experts are
essential. Establishing privacy guidelines, standards, and best practices
specific to embedded tags can help ensure responsible and privacy-preserving
deployment.

###  8.3 Embedded Markers in Consumer Products

One of the main applications of embedded machine-readable markers is the
concept of Digital Product Passports (DPP). DPPs serve as comprehensive
digital records that accompany physical consumer products throughout their
lifecycle, providing valuable information and insights to various
stakeholders, including manufacturers, retailers, and consumers [71, 145].

By embedding machine-readable markers into products during the manufacturing
process, a wealth of information can be captured and associated with each
item. This information can include details about the product’s origin,
materials used, manufacturing processes, quality certifications, and
sustainability attributes. Thus, DPPs could allow the metadata to be securely
stored and accessed throughout the product’s journey.

For manufacturers, DPPs could enable enhanced traceability, supply chain
management, and quality control. Manufacturers could monitor the entire
lifecycle of their products, ensuring compliance with regulations, identifying
potential issues, and tracking product performance. This comprehensive data
could enable manufacturers to make informed decisions, optimize processes, and
improve sustainability practices.

Retailers and distributors could also benefit from DPPs. By accessing the
embedded metadata, retailers can verify product authenticity, ensure
compliance with standards, and provide consumers with detailed information
about the products they offer. This transparency builds trust and enables
consumers to make more informed purchasing decisions based on their
preferences and values.

Consumers may make sustainable and ethical choices, supporting brands that
align with their values, using the information from the embedded markers.
Additionally, DPPs can facilitate after-sales services, such as warranty
registration, maintenance guides, and product recalls, enhancing the overall
user experience.

However, the implementation of DPPs faces certain challenges. Data privacy and
security are key concerns, as sensitive information is associated with each
product. Robust measures must be in place to ensure the secure and controlled
access to this information, protecting both the integrity of the data and the
privacy of individuals. Additionally, the standardization and interoperability
of metadata formats and protocols are essential to enable seamless exchange of
information across different stakeholders and systems.

###  8.4 Interaction and Personalization in AR

Enabling the detection of physical objects’ metadata on AR headsets, such as
smart glasses, holds great potential for advancing personalized AR
experiences. By incorporating sensing capabilities into these headsets, users
can effortlessly interact with their surroundings and receive additional
context and information about tagged objects. The continuous scanning of the
environment using always-on sensors would eliminate the need for users to
point their mobile devices in specific directions to read the tags.

However, it is essential to display the information from the marker only when
it is relevant and beneficial to the user. Avoiding information overload is
crucial to prevent distractions and maintain focus. Inspired by previous
research [83], an optimization-based approach could be pursued to determine
which tags should be visible in the AR view. Factors such as the user’s gaze,
attention level, and current activity will be taken into consideration.
Leveraging tools and sensors already present in AR headsets, such as eye
tracking, will aid in the computational determination of information
visibility in the AR environment. This personalized approach ensures that
users receive the most relevant and contextually appropriate information.

Figure 8.1: Adaptive display of related information acquired from unobtrusive
tags could be utilized to create a more personalized and assistive mixed
reality environment.

Moreover, the AR content displayed can be tailored to the user’s daily tasks
and goals, further enhancing personalization [28]. For instance, a tagged
artwork in a museum can trigger personalized information about the artist, the
painting’s history, or related artworks, providing a more engaging and
informative experience for each visitor. When shopping, the embedded tags on
product packaging can be spatially displayed and filtered based on the user’s
predetermined shopping list and fitness goals (Figure 8.1). This tailored
approach would provide users with targeted recommendations and relevant
information that aligns with their specific needs and preferences. Throughout
these interactions, ensuring privacy and obtaining user consent should be
prioritized (Section 8.2).

##  Chapter 9 Conclusion

In this thesis, we have explored the design, fabrication, and detection of
embedded machine-readable markers that serve as a bridge between the physical
and virtual worlds. These tags offer a means to identify, track, and interact
with real-world objects in mixed reality and spatial computing. Our research
explores three distinct approaches: natural markers, structural markers, and
internal markers.

Natural markers utilize the inherent fingerprints of objects and materials.
Structural markers leverage the structural artifacts that emerge during the
fabrication process for unique identification. Internal markers are
intentionally engineered within the interior of objects and are made from
specialized materials. These approaches have been optimized for minimal impact
on the object’s appearance, cost-effective fabrication using commonly
available tools like 3D printers and laser cutters, and detection using
readily accessible sensors such as RGB and near-infrared cameras.

Our research envisions a future where every real-world object carries metadata
that can be accessed effortlessly using mobile devices or augmented reality
glasses, a concept we refer to as "ubiquitous metadata." By combining
techniques from computer vision, machine learning, computational imaging, and
material science, our approaches provide robust and versatile solutions toward
this vision.

In conclusion, our work brings us closer to a future where real-world objects
are seamlessly integrated into virtual worlds. By embedding embedded machine-
readable markers into real-world objects, we empower users with a wealth of
multimedia content, interactive experiences, and contextual information. The
implications and applications of our research span a wide range of domains,
including product design, manufacturing, entertainment, marketing, logistics,
security, and sustainability. With responsible implementation, we can
revolutionize object identification and interaction, paving the way for an
interconnected and intelligent future.

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