Not Every Side Is Equal: Localization Uncertainty Estimation for Semi-Supervised 3D Object Detection

This repository contains an implementation of Nesie, a Semi-Supervised 3D Object Detection method introduced in our paper:

[Project Webpage] [Paper]

News

• 18. July 2023: Nesie is accepted at ICCV 2023. 🔥
• October 2023: Nesie PDF released.
• December 2023: Code for ScanNet dataset released.
• December 2023: Code for SunRGB-D dataset will be released soon.

Installation

Please following the env_setup.sh

Getting Started

We follow the mmdetection3d data preparation protocol described in scannet, sunrgbd.

Pre-training

To start pre-training, run with Nesie configs:

CUDA_VISIBLE_DEVICES=$gpu_id OMP_NUM_THREADS=24 \
python tools/train.py \
configs/Nesie/nesie-votenet-scannet-pretrain-$Ratio.py \
--gpu-ids 0 

Training

To start training, run with Nesie configs:

CUDA_VISIBLE_DEVICES=$gpu_id OMP_NUM_THREADS=24 \
python tools/train.py \
configs/Nesie/nesie-votenet-scannet-train-$Ratio.py \
--gpu-ids 0 \
--load-from work_dirs/nesie-votenet-scannet-pretrain-$Ratio/epoch_36.pth

Testing

Test pre-trained model using with Nesie configs:

CUDA_VISIBLE_DEVICES=$gpu_id OMP_NUM_THREADS=24 \
python tools/test.py \
configs/Nesie/nesie-votenet-scannet-test.py \
work_dirs/nesie-votenet-scannet-train-$Ratio/epoch_36.pth --eval mAP --seed 9

Results

Comparison with state-of-the-art methods

Visulization of Detection Results

Citation

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

@InProceedings{Wang_2023_ICCV,
    author    = {ChuXin Wang and Wenfei Yang and Tianzhu Zhang},
    title     = {Not Every Side Is Equal: Localization Uncertainty Estimation for Semi-Supervised 3D Object Detection},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2023},
}