PACE: A Large-Scale Dataset with Pose Annotations in Cluttered Environments

• URL: http://arxiv.org/abs/2312.15130v3
• Date: Fri, 19 Jul 2024 16:28:09 GMT
• Title: PACE: A Large-Scale Dataset with Pose Annotations in Cluttered Environments
• Authors: Yang You, Kai Xiong, Zhening Yang, Zhengxiang Huang, Junwei Zhou, Ruoxi Shi, Zhou Fang, Adam W. Harley, Leonidas Guibas, Cewu Lu,
• Abstract summary: PACE (Pose s in Cluttered Environments) is a large-scale benchmark for pose estimation methods in cluttered scenarios. The benchmark consists of 55K frames with 258K annotations across 300 videos, covering 238 objects from 43 categories. PACE-Sim contains 100K photo-realistic simulated frames with 2.4M annotations across 931 objects.
• Score: 50.79058028754952
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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