Omni6D: Large-Vocabulary 3D Object Dataset for Category-Level 6D Object Pose Estimation

TL;DR

Omni6D is a large, diverse RGBD dataset with 166 categories and over 0.8 million captures, designed to advance category-level 6D object pose estimation in realistic scenarios.

Contribution

The paper introduces Omni6D, a comprehensive dataset with extensive categories and benchmarks, and proposes a symmetry-aware metric and fine-tuning method for improved pose estimation.

Findings

Benchmarking of existing algorithms on Omni6D

Introduction of a symmetry-aware evaluation metric

Effective fine-tuning approach for large vocabulary adaptation

Abstract

6D object pose estimation aims at determining an object's translation, rotation, and scale, typically from a single RGBD image. Recent advancements have expanded this estimation from instance-level to category-level, allowing models to generalize across unseen instances within the same category. However, this generalization is limited by the narrow range of categories covered by existing datasets, such as NOCS, which also tend to overlook common real-world challenges like occlusion. To tackle these challenges, we introduce Omni6D, a comprehensive RGBD dataset featuring a wide range of categories and varied backgrounds, elevating the task to a more realistic context. 1) The dataset comprises an extensive spectrum of 166 categories, 4688 instances adjusted to the canonical pose, and over 0.8 million captures, significantly broadening the scope for evaluation. 2) We introduce a symmetry-aware metric and conduct systematic benchmarks of existing algorithms on Omni6D, offering a thorough exploration of new challenges and insights. 3) Additionally, we propose an effective fine-tuning approach that adapts models from previous datasets to our extensive vocabulary setting. We believe this initiative will pave the way for new insights and substantial progress in both the industrial and academic fields, pushing forward the boundaries of general 6D pose estimation.