PyTorch implemention of ICCV'21 paper SGPA: Structure-Guided Prior Adaptation for Category-Level 6D Object Pose Estimation

Related tags

Deep LearningSGPA
Overview

SGPA: Structure-Guided Prior Adaptation for Category-Level 6D Object Pose Estimation

This is the PyTorch implemention of ICCV'21 paper SGPA: Structure-Guided Prior Adaptation for Category-Level 6D Object Pose Estimation by Kai Chen and Qi Dou.

intro

Abstract

Category-level 6D object pose estimation aims to predict the position and orientation for unseen objects, which plays a pillar role in many scenarios such as robotics and augmented reality. The significant intra-class variation is the bottleneck challenge in this task yet remains unsolved so far. In this paper, we take advantage of category prior to overcome this problem by innovating a structure-guided prior adaptation scheme to accurately estimate 6D pose for individual objects. Different from existing prior based methods, given one object and its corresponding category prior, we propose to leverage their structure similarity to dynamically adapt the prior to the observed object. The prior adaptation intrinsically associates the adopted prior with different objects, from which we can accurately reconstruct the 3D canonical model of the specific object for pose estimation. To further enhance the structure characteristic of objects, we extract low-rank structure points from the dense object point cloud, therefore more efficiently incorporating sparse structural information during prior adaptation. Extensive experiments on CAMERA25 and REAL275 benchmarks demonstrate significant performance improvement.

Requirements

  • Linux (tested on Ubuntu 18.04)
  • Python 3.6+
  • CUDA 10.0
  • PyTorch 1.1.0

Installation

Conda virtual environment

We recommend using conda to setup the environment.

If you have already installed conda, please use the following commands.

conda create -n sgpa python=3.6
conda activate sgpa
pip install -r requirements.txt

Build PointNet++

cd SGPA/pointnet2/pointnet2
python setup.py install

Build nn_distance

cd SGPA/lib/nn_distance
python setup.py install

Dataset

Download camera_train, camera_val, real_train, real_test, ground-truth annotations and mesh models provided by NOCS.

Then, organize and preprocess these files following SPD. For a quick evaluation, we provide the processed testing data for REAL275. You can download it here and organize the testing data as follows:

SGPA
├── data
│   └── Real
│       ├──test
│       └──test_list.txt
└── results
    └── mrcnn_results
        └──real_test

Evaluation

Please download our trained model here and put it in the 'SGPA/model' directory. Then, you can have a quick evaluation on the REAL275 dataset using the following command.

bash eval.sh

Train

In order to train the model, remember to download the complete dataset, organize and preprocess the dataset properly at first.

train.py is the main file for training. You can simply start training using the following command.

bash train.sh

Citation

If you find the code useful, please cite our paper.

@inproceedings{chen2021sgpa,
  title={Sgpa: Structure-guided prior adaptation for category-level 6d object pose estimation},
  author={Chen, Kai and Dou, Qi},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={2773--2782},
  year={2021}
}

Any questions, please feel free to contact Kai Chen ([email protected]).

Acknowledgment

The dataset is provided by NOCS. Our code is developed based on SPD and Pointnet2.PyTorch.

Owner
Chen Kai
Chen Kai
A new test set for ImageNet

ImageNetV2 The ImageNetV2 dataset contains new test data for the ImageNet benchmark. This repository provides associated code for assembling and worki

186 Dec 18, 2022
Adversarial Learning for Modeling Human Motion

Adversarial Learning for Modeling Human Motion This repository contains the open source code which reproduces the results for the paper: Adversarial l

wangqi 6 Jun 15, 2021
VoxHRNet - Whole Brain Segmentation with Full Volume Neural Network

VoxHRNet This is the official implementation of the following paper: Whole Brain Segmentation with Full Volume Neural Network Yeshu Li, Jonathan Cui,

Microsoft 12 Nov 24, 2022
Concept drift monitoring for HA model servers.

{Fast, Correct, Simple} - pick three Easily compare training and production ML data & model distributions Goals Boxkite is an instrumentation library

98 Dec 15, 2022
A pytorch reproduction of { Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation }.

A PyTorch Reproduction of HCN Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation. Ch

Guyue Hu 210 Dec 31, 2022
Automatic deep learning for image classification.

AutoDL AutoDL automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few line

wenqi 2 Oct 12, 2022
Discovering Dynamic Salient Regions with Spatio-Temporal Graph Neural Networks

Discovering Dynamic Salient Regions with Spatio-Temporal Graph Neural Networks This is the official code for DyReg model inroduced in Discovering Dyna

Bitdefender Machine Learning 11 Nov 08, 2022
PyTorch code of paper "LiVLR: A Lightweight Visual-Linguistic Reasoning Framework for Video Question Answering"

LiVLR-VideoQA We propose a Lightweight Visual-Linguistic Reasoning framework (LiVLR) for VideoQA. The overview of LiVLR: Evaluation on MSRVTT-QA Datas

JJ Jiang 7 Dec 30, 2022
SuperSDR: multiplatform KiwiSDR + CAT transceiver integrator

SuperSDR SuperSDR integrates a realtime spectrum waterfall and audio receive from any KiwiSDR around the world, together with a local (or remote) cont

Marco Cogoni 30 Nov 29, 2022
Pyeventbus: a publish/subscribe event bus

pyeventbus pyeventbus is a publish/subscribe event bus for Python 2.7. simplifies the communication between python classes decouples event senders and

15 Apr 21, 2022
Intrusion Test Tool with Python

P3ntsT00L Uma ferramenta escrita em Python, feita para Teste de intrusão. Requisitos ter o python 3.9.8 instalado em sua máquina. ter a git instalada

josh washington 2 Dec 27, 2021
VolumeGAN - 3D-aware Image Synthesis via Learning Structural and Textural Representations

VolumeGAN - 3D-aware Image Synthesis via Learning Structural and Textural Representations 3D-aware Image Synthesis via Learning Structural and Textura

GenForce: May Generative Force Be with You 116 Dec 26, 2022
Rapid experimentation and scaling of deep learning models on molecular and crystal graphs.

LitMatter A template for rapid experimentation and scaling deep learning models on molecular and crystal graphs. How to use Clone this repository and

Nathan Frey 32 Dec 06, 2022
Model serving at scale

Run inference at scale Cortex is an open source platform for large-scale machine learning inference workloads. Workloads Realtime APIs - respond to pr

Cortex Labs 7.9k Jan 06, 2023
Repository for the Bias Benchmark for QA dataset.

BBQ Repository for the Bias Benchmark for QA dataset. Authors: Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Tho

ML² AT CILVR 18 Nov 18, 2022
PyTorch implementation of the paper: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features

Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features Estimate the noise transition matrix with f-mutual information. This co

<a href=[email protected]"> 1 Jun 05, 2022
Code and data for the EMNLP 2021 paper "Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts". Coming soon!

ToxiChat Code and data for the EMNLP 2021 paper "Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts". Install depen

Ashutosh Baheti 11 Jan 01, 2023
Model that predicts the probability of a Twitter user being anti-vaccination.

stylebody {text-align: justify}/style AVAXTAR: Anti-VAXx Tweet AnalyzeR AVAXTAR is a python package to identify anti-vaccine users on twitter. The

10 Sep 27, 2022
AI Toolkit for Healthcare Imaging

Medical Open Network for AI MONAI is a PyTorch-based, open-source framework for deep learning in healthcare imaging, part of PyTorch Ecosystem. Its am

Project MONAI 3.7k Jan 07, 2023
ML-Ensemble – high performance ensemble learning

A Python library for high performance ensemble learning ML-Ensemble combines a Scikit-learn high-level API with a low-level computational graph framew

Sebastian Flennerhag 764 Dec 31, 2022