README.md

---
license: cc-by-nc-4.0
---

## ⛳ NeRF-MAE Dataset

Download the preprocessed datasets here. 

- Pretraining dataset (comprising NeRF radiance and density grids). [Download link](https://s3.amazonaws.com/tri-ml-public.s3.amazonaws.com/github/nerfmae/NeRF-MAE_pretrain.tar.gz)
- Finetuning dataset (comprising NeRF radiance and density grids and bounding box/semantic labelling annotations). [3D Object Detection (Provided by NeRF-RPN)](https://drive.google.com/drive/folders/1q2wwLi6tSXu1hbEkMyfAKKdEEGQKT6pj), [3D Semantic Segmentation (Coming Soon)](), [Voxel-Super Resolution (Coming Soon)]()


Extract pretraining and finetuning dataset under ```NeRF-MAE/datasets```. The directory structure should look like this:

```
NeRF-MAE
├── pretrain
│   ├── features
│   └── nerfmae_split.npz
└── finetune
    └── front3d_rpn_data
        ├── features
        ├── aabb
        └── obb
```

**For more details, dataloaders and how to use this dataset**: see our Github repo: https://github.com/zubair-irshad/NeRF-MAE

**Coming Soon**: Multi-view rendered images and Instant-NGP checkpoints (totalling 3200+ trained NeRF checkpoints and over 1M images)

Note: The above datasets are all you need to train and evaluate our method. Bonus: we will be releasing our multi-view rendered posed RGB images from FRONT3D, HM3D and Hypersim as well as Instant-NGP trained checkpoints soon (these comprise over 1.6M+ images and 3200+ NeRF checkpoints)

Please note that our dataset was generated using the instruction from [NeRF-RPN]([NeRF-RPN](https://github.com/lyclyc52/NeRF_RPN)) and [3D-CLR](https://vis-www.cs.umass.edu/3d-clr/). Please consider citing our work, NeRF-RPN and 3D-CLR if you find this dataset useful in your research. 

Please also note that our dataset uses [Front3D](https://arxiv.org/abs/2011.09127), [Habitat-Matterport3D](https://arxiv.org/abs/2109.08238), [HyperSim](https://github.com/apple/ml-hypersim) and [ScanNet](https://www.scan-net.org/) as the base version of the dataset i.e. we train a NeRF per scene and extract radiance and desnity grid as well as aligned NeRF-grid 3D annotations. Please read the term of use for each dataset if you want to utilize the posed multi-view images for each of these datasets.