  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.
