PACE: Pose Annotations in Cluttered Environments
Yang You*, kai xiong, Zhening Yang, Zhengxiang Huang, Junwei Zhou, Ruoxi Shi, Zhou FANG, Adam Harley
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 PACE-Sim, 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. Our benchmark code and data is available on https://github.com/qq456cvb/PACE."
BibTeX
@inproceedings{eccv2024_paceposeannotati,
title = {PACE: Pose Annotations in Cluttered Environments},
author = {Yang You* and kai xiong and Zhening Yang and Zhengxiang Huang and Junwei Zhou and Ruoxi Shi and Zhou FANG and Adam Harley and Leonidas Guibas and Cewu Lu*},
booktitle = {ECCV 2024},
year = {2024}
}