[CVPR 2024]
Benchmarking Implicit Neural Representation and Geometric Rendering in Real-Time RGB-D SLAM

Project Page | Paper

Installation

First you have to make sure that you have all dependencies in place. The simplest way to do so, is to use anaconda.

For linux, you need to install libopenexr-dev before creating the environment:

sudo apt-get install libopenexr-dev

You can then create an anaconda environment called benchmark-release:

git clone https://github.com/thua919/NeRF-SLAM-Benchmark-CVPR24.git
cd NeRF-SLAM-Benchmark-CVPR24
conda env create -f environment.yaml
conda activate benchmark-release

Alternatively, if you run into installation errors, we recommend the following commands:

# Create conda environment
conda create -n benchmark-release python=3.7
conda activate benchmark-release

# Install the pytorch first (Please check the cuda version)
pip install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1 -f https://download.pytorch.org/whl/cu113/torch_stable.html

# Install all the dependencies via pip (Note here pytorch3d and tinycudann requires ~10min to build)
pip install -r requirements.txt

# For tinycudann, if you cannot access network when you use GPUs, you can also try build from source as below:
git clone --recursive https://github.com/nvlabs/tiny-cuda-nn
cd tiny-cuda-nn/bindings/torch
python setup.py install

# Build extension (marching cubes from neuralRGBD)
cd external/NumpyMarchingCubes
python setup.py install

Quick Run

Download the data as below and the data is saved into the Datasets/ folder,

mkdir -p Datasets
cd Datasets
# Replica Dataset for Lab Scenario
wget https://cvg-data.inf.ethz.ch/nice-slam/data/Replica.zip
unzip Replica.zip
# NeuralRGBD Dataset for Practical Scenario
wget http://kaldir.vc.in.tum.de/neural_rgbd/neural_rgbd_data.zip
unzip neural_rgbd_data.zip

and then you can run benchmark for, e.g. room0 sequence of Replica Dataset by:

python run_benchmark.py configs/Replica/room0.yaml

Evaluation

ATE/PSNR/DepthL1/Time

To monitor the evaluation metrics throughout SLAM running, please use tensorboard:

tensorboard --logdir output/Replica/room0/ --port=8008

Acc./Comp.

Upon finishing the SLAM process, you will see the final reconstruction in the folder with .ply ending. To measure the quality of the reconstruction, we recommend you to evaluate the meshes using the repository here. Note that our results are evaluated by its Neural-RGBD/GO-Surf mesh culling strategy:

## Replica ##
# mesh culling
INPUT_MESH=/home/ps/data/tongyanhua/NeRF-SLAM-Benchmark-CVPR24/Replica/room0/mesh_track1999.ply # path_to_your_mesh
python cull_mesh.py --config configs/Replica/room0.yaml --input_mesh $INPUT_MESH --remove_occlusion --gt_pose
# mesh evaluation
REC_MESH=/home/ps/data/tongyanhua/NeRF-SLAM-Benchmark-CVPR24/Replica/room0/mesh_track1999_cull_occlusion.ply # path_to_your_culled_mesh
GT_MESH=/home/ps/data/tongyanhua/Datasets/Replica/room0_mesh.ply # path_to_gt_mesh: we use gt mesh without any culling for replica evaluation
python eval_recon.py --rec_mesh $REC_MESH --gt_mesh $GT_MESH --dataset_type Replica -3d


## Neural RGBD ##
INPUT_MESH=/home/ps/data/tongyanhua/NeRF-SLAM-Benchmark-CVPR24/synthetic/gr_mesh_track1441.ply
python cull_mesh.py --config configs/Synthetic/gr.yaml --input_mesh $INPUT_MESH --remove_occlusion --gt_pose
REC_MESH=/home/ps/data/tongyanhua/NeRF-SLAM-Benchmark-CVPR24/synthetic/gr_mesh_track1441_cull_occlusion.ply
GT_MESH=/home/ps/data/tongyanhua/Datasets/neural_rgbd_data/green_room/gt_mesh_cull_virt_cams.ply # path_to_gt_mesh: we use culled gt mesh for neuralrgbd evaluation
python eval_recon.py --rec_mesh $REC_MESH --gt_mesh $GT_MESH --dataset_type RGBD -3d

You may need to download ground truth meshes by youself, following the instruction and data directories of section Run Evaluation in the repository.

ScanNet for Explicit Hybrid Encoding

Please find our implementation here.

Related Repositories

We would like to extend our gratitude to the authors of NICE-SLAM, ESLAM, COSLAM, TensoRF, and NeuS for their exceptional work.

Citing

If you find our work useful, please consider citing:

@inproceedings{nerfslam24hua,
  author = {Tongyan Hua and Lin Wang},
  title = {Benchmarking Implicit Neural Representation and Geometric Rendering in Real-Time RGB-D SLAM},
  booktitle = {Proceedings of the IEEE international conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2024},
}

Acknowledgement

This paper is supported by the National Natural Science Foundation of China (NSF).

thua919/NeRF-SLAM-Benchmark-CVPR24

[CVPR 2024] Benchmarking Implicit Neural Representation and Geometric Rendering in Real-Time RGB-D SLAM Project Page | Paper