Omni6DPose: A Benchmark and Model for Universal 6D Object Pose Estimation and Tracking

TL;DR

Omni6DPose introduces a large-scale, diverse dataset for 6D object pose estimation and tracking, along with an improved model, GenPose++, to advance research in this challenging field.

Contribution

The paper presents Omni6DPose, a comprehensive dataset with real and simulated images across many categories, and introduces GenPose++, a novel pose estimation framework with enhanced features.

Findings

Benchmark results highlight current method limitations.

GenPose++ outperforms previous models on the new dataset.

The dataset enables more robust evaluation of pose estimation methods.

Abstract

6D Object Pose Estimation is a crucial yet challenging task in computer vision, suffering from a significant lack of large-scale datasets. This scarcity impedes comprehensive evaluation of model performance, limiting research advancements. Furthermore, the restricted number of available instances or categories curtails its applications. To address these issues, this paper introduces Omni6DPose, a substantial dataset characterized by its diversity in object categories, large scale, and variety in object materials. Omni6DPose is divided into three main components: ROPE (Real 6D Object Pose Estimation Dataset), which includes 332K images annotated with over 1.5M annotations across 581 instances in 149 categories; SOPE(Simulated 6D Object Pose Estimation Dataset), consisting of 475K images created in a mixed reality setting with depth simulation, annotated with over 5M annotations across 4162 instances in the same 149 categories; and the manually aligned real scanned objects used in both ROPE and SOPE. Omni6DPose is inherently challenging due to the substantial variations and ambiguities. To address this challenge, we introduce GenPose++, an enhanced version of the SOTA category-level pose estimation framework, incorporating two pivotal improvements: Semantic-aware feature extraction and Clustering-based aggregation. Moreover, we provide a comprehensive benchmarking analysis to evaluate the performance of previous methods on this large-scale dataset in the realms of 6D object pose estimation and pose tracking.