pytorch implementation of GPV-Pose

Last update: Dec 01, 2022

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Overview

GPV-Pose

Pytorch implementation of GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting. (link)

UPDATE

A new version of code which integrates shape prior information will be updated to the shape-prior-integrated branch in this repo soon.

Required environment

Ubuntu 18.04
Python 3.8
Pytorch 1.10.1
CUDA 11.3.

Installing

Install the main requirements in 'requirement.txt'.
Install Detectron2.

Data Preparation

To generate your own dataset, use the data preprocess code provided in this git. Download the detection results in this git.

Trained model

Download the trained model from this link.

Training

Please note, some details are changed from the original paper for more efficient training.

Specify the dataset directory and run the following command.

python -m engine.train --data_dir YOUR_DATA_DIR --model_save SAVE_DIR

Detailed configurations are in 'config/config.py'.

Evaluation

python -m evaluation.evaluate --data_dir YOUR_DATA_DIR --detection_dir DETECTION_DIR --resume 1 --resume_model MODEL_PATH --model_save SAVE_DIR

Acknowledgment

Our implementation leverages the code from 3dgcn, FS-Net, DualPoseNet, SPD.

pytorch implementation of GPV-Pose

Related tags

Overview

GPV-Pose

UPDATE

Required environment

Installing

Data Preparation

Trained model

Training

Evaluation

Acknowledgment

Owner

💛 Code and Dataset for our EMNLP 2021 paper: "Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes"

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