SamsungLabs / imvoxelnet

[WACV2022] ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection
MIT License
274 stars 29 forks source link
3d-object-detection imvoxelnet kitti mmdetection nuscenes object-detection pytorch scannet sun-rgbd

PWC PWC

ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection

News:

This repository contains implementation of the monocular/multi-view 3D object detector ImVoxelNet, introduced in our paper:

ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection
Danila Rukhovich, Anna Vorontsova, Anton Konushin
Samsung Research
https://arxiv.org/abs/2106.01178

drawing

Installation

For convenience, we provide a Dockerfile. Alternatively, you can install all required packages manually.

This implementation is based on mmdetection3d framework. Please refer to the original installation guide install.md, replacing open-mmlab/mmdetection3d with saic-vul/imvoxelnet. Also, rotated_iou should be installed with these 4 commands.

Most of the ImVoxelNet-related code locates in the following files: detectors/imvoxelnet.py, necks/imvoxelnet.py, dense_heads/imvoxel_head.py, pipelines/multi_view.py.

Datasets

We support three benchmarks based on the SUN RGB-D dataset.

For ScanNet please follow instructions in scannet. For KITTI and nuScenes, please follow instructions in getting_started.md.

Getting Started

Please see getting_started.md for basic usage examples.

Training

To start training, run dist_train with ImVoxelNet configs:

bash tools/dist_train.sh configs/imvoxelnet/imvoxelnet_kitti.py 8

Testing

Test pre-trained model using dist_test with ImVoxelNet configs:

bash tools/dist_test.sh configs/imvoxelnet/imvoxelnet_kitti.py \
    work_dirs/imvoxelnet_kitti/latest.pth 8 --eval mAP

Visualization

Visualizations can be created with test script. For better visualizations, you may set score_thr in configs to 0.15 or more:

python tools/test.py configs/imvoxelnet/imvoxelnet_kitti.py \
    work_dirs/imvoxelnet_kitti/latest.pth --show \
    --show-dir work_dirs/imvoxelnet_kitti

Models

v2 adds center sampling for indoor scenario. v3 simplifies 3d neck for indoor scenario. Differences are discussed in v2 and v3 preprints.

Dataset Object Classes Version Download
SUN RGB-D 37 from
Total3dUnderstanding
v1 | mAP@0.15: 41.5
v2 | mAP@0.15: 42.7
v3 | mAP@0.15: 43.7
model | log | config
model | log | config
model | log | config
SUN RGB-D 30 from
PerspectiveNet
v1 | mAP@0.15: 44.9
v2 | mAP@0.15: 47.2
v3 | mAP@0.15: 48.7
model | log | config
model | log | config
model | log | config
SUN RGB-D 10 from VoteNet v1 | mAP@0.25: 38.8
v2 | mAP@0.25: 39.4
v3 | mAP@0.25: 40.7
model | log | config
model | log | config
model | log | config
ScanNet 18 from VoteNet v1 | mAP@0.25: 40.6
v2 | mAP@0.25: 45.7
v3 | mAP@0.25: 48.1
model | log | config
model | log | config
model | log | config
KITTI Car v1 | AP@0.7: 17.8 model | log | config
nuScenes Car v1 | AP: 51.8 model | log | config

Example Detections

drawing

Citation

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

@inproceedings{rukhovich2022imvoxelnet,
  title={Imvoxelnet: Image to voxels projection for monocular and multi-view general-purpose 3d object detection},
  author={Rukhovich, Danila and Vorontsova, Anna and Konushin, Anton},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={2397--2406},
  year={2022}
}