PACE: Pose Annotations in Cluttered Environments

European Conference on Computer Vision (ECCV) 2024

Fri Oct 4, 10:30am-12:30pm CEST, Poster#191, Exhibition Area

Yang You1, Kai Xiong2, Zhening Yang3, Zhengxiang Huang2, Junwei Zhou2, Ruoxi Shi2, Zhou Fang2 Adam W Harley1 Leonidas Guibas1 Cewu Lu2
1Stanford University 2Shanghai Jiao Tong University 3Horizon Robotics
Paper Video Code Data

Abstract

We introduce PACE (Pose Annotations in Cluttered Environments), a large-scale benchmark designed to advance the development and evaluation of pose estimation methods in cluttered scenarios. PACE provides a large-scale real-world benchmark for both instance-level and category-level settings. The benchmark consists of 55K frames with 258K annotations across 300 videos, covering 238 objects from 43 categories and featuring a mix of rigid and articulated items in cluttered scenes. To annotate the real-world data efficiently, we develop an innovative annotation system with a calibrated 3-camera setup. Additionally, we offer PACESim, which contains 100K photo-realistic simulated frames with 2.4M annotations across 931 objects. We test state-of-the-art algorithms in PACE along two tracks: pose estimation, and object pose tracking, revealing the benchmark’s challenges and research opportunities.

Annotation Visualization

Image 1

RGB

Image 2

Rendered Object

Image 3

Object Pose

Image 4

Depth

Image 5

NOCS Map

Image 6

Instance Mask

Image 1

RGB

Image 2

Rendered Object

Image 3

Object Pose

Image 4

Depth

Image 5

NOCS Map

Image 6

Instance Mask

Image 1

RGB

Image 2

Rendered Object

Image 3

Object Pose

Image 4

Depth

Image 5

NOCS Map

Image 6

Instance Mask

Image 1

RGB

Image 2

Rendered Object

Image 3

Object Pose

Image 4

Depth

Image 5

NOCS Map

Image 6

Instance Mask

Image 1

RGB

Image 2

Rendered Object

Image 3

Object Pose

Image 4

Depth

Image 5

NOCS Map

Image 6

Instance Mask

Dataset Comparison

Data Distribution

Pose Annotation Distrubtion

Object Instance Distrubtion

Object Size Distrubtion

Azimuth and Elevation Distrubtion

Occlusion Distrubtion

Benchmarks

Instance-level Pose Estimation

Category-level Pose Estimation

Model-free Pose Tracking

Model-based Pose Tracking

Data Collection Pipeline

BibTeX


@misc{you2023pace,
    title={PACE: Pose Annotations in Cluttered Environments},
    author={You, Yang and Xiong, Kai and Yang, Zhening and Huang, Zhengxiang and Zhou, Junwei and Shi, Ruoxi and Fang, Zhou and Harley, Adam W. and Guibas, Leonidas and Lu, Cewu},
    booktitle={European Conference on Computer Vision},
    year={2024},
    organization={Springer}
}