3D Small Object Detection with Dynamic Spatial Pruning

Introduction

This repo contains PyTorch implementation for paper 3D Small Object Detection with Dynamic Spatial Pruning based on MMDetection3D.

3D Small Object Detection with Dynamic Spatial Pruning
Xiuwei Xu*, Zhihao Sun*, Ziwei Wang, Hongmin Liu, Jie Zhou, Jiwen Lu

News

• [2023/6/04]: We transfer DSPDet3D to extremely large scenes and show great performance! Demo will be released in our project page.
• [2023/5/01]: Code release.

Method

Overall pipeline of DSPDet3D:

Getting Started

For data preparation and environment setup:

• Installation
• Prepare Dataset

For training and evaluation:

• Train, Eval and Visualize

Main Results

We provide the checkpoints for quick reproduction of the results reported in the paper. The pruning threshold can be adjusted freely to tradeoff between accuracy and efficiency without any finetuning.

Benchmark	mAP@0.25	mAP@0.5	Downloads
ScanNet-md40	65.25	53.66	model
TO-SCENE-down	63.67	55.71	model

Comparison with state-of-the-art methods on TO-SCENE dataset:

Visualization results on ScanNet:

Visualization results on Matterport3D:

Acknowledgement

We thank a lot for the flexible codebase of FCAF3D and valuable datasets provided by ScanNet and TO-SCENE.

Bibtex

If this work is helpful for your research, please consider citing the following BibTeX entry.

@article{xu2023dsp, 
      title={3D Small Object Detection with Dynamic Spatial Pruning}, 
      author={Xiuwei Xu and Zhihao Sun and Ziwei Wang and Hongmin Liu and Jie Zhou and Jiwen Lu},
      journal={arXiv preprint arXiv:2305.03716},
      year={2023}
}