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Robust-3D-Tracking-with-Quality-Aware-Shape-Completion

This repository contains the implementation of our method for 3D single object tracking with shape completion. Our approach focuses on constructing precise shape representations using dense and complete point clouds, achieved through shape completion techniques. The provided code includes a voxelized 3D tracking framework with a quality-aware shape completion mechanism, as well as modules for relation modeling.

https://doi.org/10.1609/aaai.v38i7.28544

Setup

Installation

  • Install pytorch

    conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=11.0 -c pytorch

  • Install other dependencies:

    pip install -r requirement.txt

Training & Testing

To train a model, you must specify the .yaml file with --cfg argument. The .yaml file contains all the configurations of the dataset and the model. We provide .yaml files under the cfgs directory.

CUDA_VISIBLE_DEVICES=0,1 python main.py  --cfg cfgs/cfg.yaml  --batch_size 64 --epoch 60 --preloading

To test a trained model, specify the checkpoint location with --checkpoint argument and send the --test flag to the command.

python main.py  --cfg cfgs/cfg.yaml  --checkpoint /path/to/checkpoint/xxx.ckpt --test

Acknowledgment

This repo is built upon M2 Track.