Skip to content

Latest commit

 

History

History
executable file
·
69 lines (53 loc) · 3.58 KB

README.md

File metadata and controls

executable file
·
69 lines (53 loc) · 3.58 KB
Raw

Boosting 3D Object Detection via Object-Focused Image Fusion

PWC

This is a MMDetection3D implementation of the paper Yang et al, "Boosting 3D Object Detection via Object-Focused Image Fusion".

Boosting 3D Object Detection via Object-Focused Image Fusion
Hao Yang*, Chen Shi*, Yihong Chen, Liwei Wang

paper

Pipeline

Prerequisites

The code is tested with Python3.7, PyTorch == 1.8, CUDA == 11.1, mmdet3d == 0.18.1, mmcv_full == 1.3.18 and mmdet == 2.14. We recommend you to use anaconda to make sure that all dependencies are in place. Note that different versions of the library may cause changes in results.

Step 1. Create a conda environment and activate it.

conda create --name demf python=3.7
conda activate demf

Step 2. Install MMDetection3D following the instruction here.

Step 3. Prepare SUN RGB-D Data following the procedure here.

Getting Started

Step 1. First we need to train a Deformable DETR on SUN RGB-D image data to get the checkpoint of image branch.

python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT train.py configs/deformdetr/imvotenet_deform.py --launcher pytorch ${@:3}

Or you can download the pre-trained image branch here.

Step 2. Specify the path to the pre-trained image branch in config.

Step 3. Train our DeMF using the following command.

python -m torch.distributed.launch --nproc_per_node=8 --master_port=$PORT train.py configs/demf/demf_votenet.py --launcher pytorch ${@:3}

We also provide pre-trained DeMF here. Use eval.py to evaluate the pretrained model and you will get the [email protected] and [email protected].

python -m torch.distributed.launch --nproc_per_node=8 --master_port=$PORT test.py --config configs/demf/demf_votenet.py --checkpoint $CHECKPOINT --eval mAP --launcher pytorch ${@:4}

Main Results

We re-implemented VoteNet and ImVoteNet, which are some improvement over the original results.

Method Point Backbone Input [email protected] [email protected]
VoteNet PointNet++ PC 60.0 41.3
ImVoteNet PointNet++ PC+RGB 64.4 43.3
DeMF(VoteNet based) PointNet++ PC+RGB 65.6 (65.3) 46.1 (45.4)
DeMF(FCAF3D based) HDResNet34 PC+RGB 67.4 (67.1) 51.2 (50.5)

DeMF (Fcaf3d based)

We release the code of the DeMF (Fcaf3d based) in DeMF_fcaf branch.

Citation

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

@misc{https://doi.org/10.48550/arxiv.2207.10589,
  author    = {Yang, Hao and Shi, Chen and Chen, Yihong and Wang, Liwei},
  title     = {Boosting 3D Object Detection via Object-Focused Image Fusion},
  publisher = {arXiv},
  year      = {2022},
}