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

TL;DR

PACE introduces a comprehensive large-scale dataset with real and simulated cluttered scene annotations to improve and evaluate pose estimation and tracking methods in complex environments.

Contribution

The paper presents PACE, a new large-scale benchmark with innovative annotation techniques, covering diverse real-world and simulated cluttered scenes for pose estimation research.

Findings

State-of-the-art algorithms face challenges in cluttered scenes.

Benchmark reveals gaps in current pose estimation methods.

Provides extensive data for future research and development.

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.