Pose Anything Anywhere: Model-Free Object Poses From Arbitrary References
MCML Authors
Junwen Huang
Dr.
Benjamin Busam
Prof. Dr.
Core PI
Abstract
Junwen Huang
Dr.
Benjamin Busam
Prof. Dr.
Core PI
Abstract
While current 6D pose estimation benchmarks have reached near-saturation on household objects, they often fail to capture the stochastic and optical complexities of industrial environments. We introduce XYZ-IBD, a high-precision benchmark for object detection and 6D pose estimation specifically designed for industrial bin-picking. XYZ-IBD addresses the domain gap by providing 75 multi-view real-world scenes containing approximately 273k annotated instances of metallic, symmetrical, and specular objects. Unlike existing datasets, our benchmark features high-density stochastic stacking and multi-instance ambiguity, reflecting authentic robotic manipulation challenges. We employ a rigorous multi-stage and semi-automatic annotation pipeline, ensuring sub-millimeter annotation accuracy. The annotations are validated through our designed error quantification scheme, securing the reliability of the annotation quality. In addition to real-world evaluation data, we provide a large-scale complementary synthetic training set that is rendered under a realistic bin-picking simulation. Benchmarking state-of-the-art (SOTA) methods for 2D detection and 6D pose estimation reveals a significant performance degradation compared to standard household benchmarks, highlighting the unsolved challenges of industrial vision. XYZ-IBD establishes a new frontier for robust pose estimation in complex, high-occlusion, and reflective scenarios.
inproceedings HHY+26
ECCV 2026
19th European Conference on Computer Vision. Malmö, Sweden, Sep 08-12, 2026. To be published. Preprint available.
Authors
J. Huang • J. Hu • P. K. Yu • S. Ilic • M. Sundermeyer • B. BusamLinks
arXiv GitHubResearch Area
BibTeXKey: HHY+26