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.
@article{arxiv.2312.15130,
title = {PACE: A Large-Scale Dataset with 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 W. Harley and Leonidas Guibas and Cewu Lu},
journal= {arXiv preprint arXiv:2312.15130},
year = {2024}
}