ImGeoNet: Image-induced Geometry-aware Voxel Representation for Multi-view 3D Object Detection

Paper link: https://arxiv.org/abs/2308.09098
Project page: https://ttaoretw.github.io/imgeonet/

Performance

Dataset	mAP@0.25	mAP@0.5	Log
ScanNet	54.57	28.94	link
ScanNet200	22.38	9.67	link
ARKitScenes	59.82	42.76	link

Performance may vary slightly depending on the number of GPUs.

Environment

# Create conda virtual environment
conda create -n imgeonet python=3.8
conda activate imgeonet

# Clone repo
git clone https://github.com/ttaoREtw/ImGeoNet.git
cd ImGeoNet

# Setup virtual environment
bash script/0_install_env.sh

Data

ScanNet & ScanNet200

Download ScanNet data and link scans folder under data/scannet, then run

# Warning: this step requires a lot of disk space
# Extract frame data: rgb, depth, intrinsic, pose, axis matrix
bash script/1a_extract_scannet_posed_data.sh

# Process scannet as in VoteNet
bash script/1b_preproc_scannet_data.sh

# Convert to mmdet3d's format
bash script/1c_convert_scannet_data.sh

ARKitScenes

# Download ARKitScenes data - 3D detection part
bash script/2a_download_arkit.sh

# Extract frame data: rgb, depth, intrinsic, pose
bash script/2b_preproc_arkit_data.sh

# Convert to mmdet3d's format
bash script/2c_convert_arkit_data.sh

Training

# Train on ScanNet, ScanNet200, and ARKitScenes
bash script/3_train_imgeonet.sh

Test

cd mmdetection3d
# config in `configs/`
# checkpoint in `work_dir/.../latest.pth`
python tools/test.py $config $checkpoint --eval mAP

Citation

@inproceedings{tu2023imgeonet,
  title     = {ImGeoNet: Image-induced Geometry-aware Voxel Representation for Multi-view 3D Object Detection},
  author    = {Tu, Tao and Chuang, Shun-Po and Liu, Yu-Lun and Sun, Cheng and Zhang, Ke and Roy, Donna and Kuo, Cheng-Hao and Sun, Min},
  booktitle = {Proceedings of the IEEE international conference on computer vision},
  year      = {2023},
}

Acknowledgement

This project is built upon various open-source projects. If your work involves components related to these projects, you may have to consider citing them.

• ARKitScenes
• ScanNet
• VoteNet
• ImVoxelNet
• MMDetection3D
• Atlas