  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.
