license: cc-by-nc-4.0
⛳ NeRF-MAE Dataset
Download the preprocessed datasets here.
- Pretraining dataset (comprising NeRF radiance and density grids). Download link
- Finetuning dataset (comprising NeRF radiance and density grids and bounding box/semantic labelling annotations). 3D Object Detection (Provided by NeRF-RPN), 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 and 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, Habitat-Matterport3D, HyperSim and ScanNet 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.