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DReg-NeRF

Official Implementation of our ICCV 2023 paper: "DReg-NeRF: Deep Registration for Neural Radiance Fields". [Project Page | ArXiv)

Given a pair of NeRF models that are trained in different coordinate frames, DReg-NeRF registers them into the same coordinate frame

1. Installation

conda create -n dreg_nerf python=3.9
conda activate dreg_nerf
cd DReg-NeRF/scripts
./scripts/env/install.sh

After installing the environment, you can try our pretrained model by following # 4.2 if you don't want to re-train all the NeRF models from scratch.

2. Fetch 3D Models from Objaverse

# Firstly, install conda env for blender:
cd DReg-NeRF/scripts/blender
pip install -r blender_environment.yml
# Then, we download models from Objaverse and render images.
./objaverse_to_blender.sh

After rendering images from 3D meshes, we put all the rendered images into images folder.

3. Train NeRF Blocks

Suppose the rendered images are put into ${DATA_ROOT_DIR}/objaverse/images.

3.1 Train

cd scripts/train
GPU_IDX=0
./train_objaverse_batch.sh true ${GPU_IDX}

After training, checkpoints/models are stored at ${DATA_ROOT_DIR}/objaverse/images/out.

3.2 Evaluation & Preprocessing

cd scripts/eval
GPU_IDX=0
./eval_objaverse_batch.sh true ${GPU_IDX}

After evaluation, results are stored at ${DATA_ROOT_DIR}/objaverse/images/eval. During evaluation, we also pre-extracted occupancy grids from the NeRF models in each block and store them at ${DATA_ROOT_DIR}/objaverse/images/out. To save disk storage, we only store model.pth, voxel_grid.pt, voxel_mask.pt and voxel_point_cloud.ply in each NeRF block models. Before training DReg-NeRF, you should check whether the trained NeRF models are good enough.

4. Train DReg-NeRF

Before training DReg-NeRF, we put the pre-extracted occupancy grids and NeRF models into a folder named nerf_models and put in at the same level to the images folder. Then, all the input required to train DReg-NeRF are stored at ${DATA_ROOT_DIR}/objaverse/nerf_models.

4.1 Train

cd scripts/train
GPU_IDX=0
./train_nerf_regtr.sh ${GPU_IDX}

After training, checkpoints are stored at ${DATA_ROOT_DIR}/out/$expname.

4.2 Evaluation

Downloading the evaluation data to test DReg-NeRF.

cd scripts/train
GPU_IDX=0
./eval_nerf_regtr.sh objaverse test 0

After evaluation, results are stored at ~/DATASETS/dreg_nerf/eval/$expname.

Cite

If you find our work useful for your research, please cite our paper as below:

@article{DBLP:journals/corr/abs-2308-09386,
  author    = {Yu Chen and
               Gim Hee Lee},
  title     = {DReg-NeRF: Deep Registration for Neural Radiance Fields},
  booktitle = {2023 {IEEE/CVF} International Conference on Computer Vision},
  pages     = {},
  year      = {2023},
}