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

Autor:	You, Yang, Xiong, Kai, Yang, Zhening, Huang, Zhengxiang, Zhou, Junwei, Shi, Ruoxi, Fang, Zhou, Harley, Adam W., Guibas, Leonidas, Lu, Cewu
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: 14 pages; Accepted to ECCV 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2312.15130 Zobrazit plný text záznamu View this record from Arxiv