Deep Bundle Adjusting Generalizable Neural Radiance Fields

Official Implementation of our CVPR 2023 paper: "DBARF: Deep Bundle-Adjusting Generalizable Neural Radiance Fields"

Our code will be released at around 1st June (I'm currently busy on my module assignments and even get barf 🤮 , I need de-barf 🙂) !

1. Installation

conda create -n dbarf python=3.9
conda activate dbarf

# install pytorch
# # CUDA 10.2
# conda install pytorch==1.11.0 torchvision==0.12.0 cudatoolkit=10.2 -c pytorch

# CUDA 11.3
conda install pytorch==1.11.0 torchvision==0.12.0 cudatoolkit=11.3 -c pytorch

# HLoc is used for extracting keypoints and matching features.
git clone --recursive https://github.com/cvg/Hierarchical-Localization/
cd Hierarchical-Localization/
python -m pip install -e .
cd ..

pip install opencv-python matplotlib easydict tqdm networkx einops imageio visdom tensorboardX configargparse lpips

2. Preprocessing

1) Extracting Scene Graph

After installing HLoc, we can extract the scene graph for each scene:

python3 -m scripts.preprocess_dbarf_dataset --dataset_dir $image_dir --outputs $output_dir --gpu_idx 0 --min_track_length 2 --max_track_length 15 --recon False --disambiguate False --visualize False

For debugging, we can also enable incremental SfM (not necessary for dbarf since our method does not rely on ground-truth camera poses) by using --recon True, removing ambiguous wrong matches by --disambiguate True, and visualizing reconstruction results by --visualize True.

2) Post-processing COLMAP Model

Also, we need to convert colmap's model into the .npy format with post-processing:

python3 -m scripts.colmap_model_to_poses_bounds --input_dir $colmap_model_dir

3. Dataset Structure