Back To Reality: Weak-supervised 3D Object Detection with Shape-guided Label Enhancement
Announcement
🔥
We have not tested the code yet. We will finish this project by April.
Introduction
This repo contains PyTorch implementation for paper Back To Reality: Weak-supervised 3D Object Detection with Shape-guided Label Enhancement (CVPR2022)
@inproceedings{xu2022br,
author = {Xu, Xiuwei and Wang, Yifan and Zheng, Yu and Rao, Yongming and Lu, Jiwen and Zhou, Jie},
title = {Back To Reality: Weak-supervised 3D Object Detection with Shape-guided Label Enhancement},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022}
}
Other papers related to 3D object detection with synthetic shape:
- RandomRooms: Unsupervised Pre-training from Synthetic Shapes and Randomized Layouts for 3D Object Detection (ICCV 2021)
New dataset
💥
We conduct additional experiment on the more challenging Matterport3D dataset. From ModelNet40 and Matterport3D, we select all 13 shared categories, each containing more than 80 object instances in Matterport3D training set, to construct our benchmark (Matterport3d-md40). Below is the performance of FSB, WSB and BR (point-version) based on Votenet: 
Note that we use OpenCV to estimate the rotated bounding boxes (RBB) as ground-truth, instead of the axis-aligned bounding boxes used in ScanNet-md40 benchmark.
ScanNet-md40 and Matterport3d-md40 are two more challenging benckmarks for indoor 3D object detection. We hope they will promote future research on small object detection and synthetic-to-real scene understanding.
Dependencies
We evaluate this code with Pytorch 1.8.1 (cuda11), which is based on the official implementation of Votenet and GroupFree3D. Please follow the requirements of them to prepare the environment. Other packages can be installed using:
pip install open3d sklearn tqdm
Current code base is tested under following environment:
- Python 3.6.13
- PyTorch 1.8.1
- numpy 1.19.2
- open3d 0.12.0
- opencv-python 4.5.1.48
- plyfile 0.7.3
- scikit-learn 0.24.1
Data preparation
ScanNet
To start from the raw data, you should:
- Follow the README under
GroupFree3D/scannetorVotenet/scannetto generate the real scenes. - Follow the README under
./data_generation/ScanNetto generate the virtual scenes.
The processed data can also be downloaded from here. They should be placed to paths:
./detection/Votenet/scannet/
./detection/GroupFree3D/scannet/
After that, the file directory should be like:
...
└── Votenet (or GroupFree3D)
├── ...
└── scannet
├── ...
├── scannet_train_detection_data_md40
├── scannet_train_detection_data_md40_obj_aug
└── scannet_train_detection_data_md40_obj_mesh_aug
Matterport3D
To start from the raw data, you should:
- Follow the README under
Votenet/scannetto generate the real scenes. - Follow the README under
./data_generation/Matterport3Dto generate the virtual scenes.
The processed data can also be downloaded from here.
The file directory should be like:
...
└── Votenet
├── ...
└── matterport
├── ...
├── matterport_train_detection_data_md40
├── matterport_train_detection_data_md40_obj_aug
└── matterport_train_detection_data_md40_obj_mesh_aug
Usage
Please follow the instructions below to train different models on ScanNet. Change --dataset scannet to --dataset matterport for training on Matterport3D.
Votenet
1. Fully-Supervised Baseline
To train the Fully-Supervised Baseline (FSB) on Scannet data:
# Recommended GPU num: 1
cd Votenet
CUDA_VISIBLE_DEVICES=0 python train_Votenet_FSB.py --dataset scannet --log_dir log_Votenet_FSB --num_point 40000
2. Weakly-Supervised Baseline
To train the Weakly-Supervised Baseline (WSB) on Scannet data:
# Recommended num of GPUs: 1
CUDA_VISIBLE_DEVICES=0 python train_Votenet_WSB.py --dataset scannet --log_dir log_Votenet_WSB --num_point 40000
3. Back To Reality
To train BR (mesh-version) on Scannet data, please run:
# Recommended num of GPUs: 2
CUDA_VISIBLE_DEVICES=0,1 python train_Votenet_BR.py --dataset scannet --log_dir log_Votenet_BRM --num_point 40000
CUDA_VISIBLE_DEVICES=0,1 python train_Votenet_BR_CenterRefine --dataset scannet --log_dir log_Votenet_BRM_Refine --num_point 40000 --checkpoint_path log_Votenet_BRM/train_BR.tar
To train BR (point-version) on Scannet data, please run:
# Recommended num of GPUs: 2
CUDA_VISIBLE_DEVICES=0,1 python train_Votenet_BR.py --dataset scannet --log_dir log_Votenet_BRP --num_point 40000 --dataset_without_mesh
CUDA_VISIBLE_DEVICES=0,1 python train_Votenet_BR_CenterRefine --dataset scannet --log_dir log_Votenet_BRP_Refine --num_point 40000 --checkpoint_path log_Votenet_BRP/train_BR.tar --dataset_without_mesh
GroupFree3D
1. Fully-Supervised Baseline
To train the Fully-Supervised Baseline (FSB) on Scannet data:
# Recommended num of GPUs: 4
cd GroupFree3D
python -m torch.distributed.launch --master_port <port_num> --nproc_per_node <num_of_gpus_to_use> train_GF_FSB.py --num_point 50000 --num_decoder_layers 6 --size_delta 0.111111111111 --center_delta 0.04 --learning_rate 0.006 --decoder_learning_rate 0.0006 --weight_decay 0.0005 --dataset scannet --log_dir log_GF_FSB --batch_size 4
2. Weakly-Supervised Baseline
To train the Weakly-Supervised Baseline (WSB) on Scannet data:
# Recommended num of GPUs: 4
python -m torch.distributed.launch --master_port <port_num> --nproc_per_node <num_of_gpus_to_use> train_GF_WSB.py --num_point 50000 --num_decoder_layers 6 --size_delta 0.111111111111 --center_delta 0.04 --learning_rate 0.006 --decoder_learning_rate 0.0006 --weight_decay 0.0005 --dataset scannet --log_dir log_GF_WSB --batch_size 4
3. Back To Reality
To train BR (mesh-version) on Scannet data, please run:
# Recommended num of GPUs: 4
python -m torch.distributed.launch --master_port <port_num> --nproc_per_node <num_of_gpus_to_use> train_GF_BR.py --num_point 50000 --num_decoder_layers 6 --size_delta 0.111111111111 --center_delta 0.04 --learning_rate 0.006 --decoder_learning_rate 0.0006 --weight_decay 0.0005 --dataset scannet --log_dir log_GF_BRM --batch_size 4
# Recommended num of GPUs: 6
python -m torch.distributed.launch --master_port <port_num> --nproc_per_node <num_of_gpus_to_use> train_GF_BR_CenterRefine.py --num_point 50000 --num_decoder_layers 6 --size_delta 0.111111111111 --center_delta 0.04 --learning_rate 0.001 --decoder_learning_rate 0.0006 --weight_decay 0.0005 --dataset scannet --log_dir log_GF_BRM_Refine --checkpoint_path <[checkpoint_path_of_groupfree3D]/ckpt_epoch_last.pth> --max_epoch 120 --val_freq 10 --save_freq 20 --batch_size 2
To train BR (point-version) on Scannet data, please run:
# Recommended num of GPUs: 4
python -m torch.distributed.launch --master_port <port_num> --nproc_per_node <num_of_gpus_to_use> train_GF_BR.py --num_point 50000 --num_decoder_layers 6 --size_delta 0.111111111111 --center_delta 0.04 --learning_rate 0.006 --decoder_learning_rate 0.0006 --weight_decay 0.0005 --dataset scannet --log_dir log_GF_BRP --batch_size 4 --dataset_without_mesh
# Recommended num of GPUs: 6
python -m torch.distributed.launch --master_port <port_num> --nproc_per_node <num_of_gpus_to_use> train_GF_BR_CenterRefine.py --num_point 50000 --num_decoder_layers 6 --size_delta 0.111111111111 --center_delta 0.04 --learning_rate 0.001 --decoder_learning_rate 0.0006 --weight_decay 0.0005 --dataset scannet --log_dir log_GF_BRP_Refine --checkpoint_path <[checkpoint_path_of_groupfree3D]/ckpt_epoch_last.pth> --max_epoch 120 --val_freq 10 --save_freq 20 --batch_size 2 --dataset_without_mesh
TODO list
We will add the following to this repo:
- Virtual scene generation for Matterport3D
- Data and code for training Votenet (both baseline and BR) on the Matterport3D dataset
Acknowledgements
We thank a lot for the flexible codebase of Votenet and GroupFree3D.
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