TRICKY 2025 HouseCat6D Object Pose Estimation Challenge with Specular and Transparent Surfaces

This paper presents the TRICKY 2025 HouseCat6D Category-Level Object Pose Estimation Challenge, held in conjunction with the ICCV 2025 workshop on Transpar-ent and Reflective Objects in the Wild. The challenge ad-dresses the critical limitations of existing pose estimation systems when applied to non-Lambertian surfaces, such as glass and metal. Leveraging the HouseCat6D dataset com-prising realistic home environments with a diverse range of transparent and specular objects, the challenge pushes state-of-the-art algorithms to estimate object pose, scale, and shape in photometrically complex scenes. Unlike traditional benchmarks focused on texture-rich, opaque objects, this challenge emphasizes robustness under reflective high-lights, refractions, and partial transparency. By promoting research in these underexplored conditions, the challenge contributes toward generalizable and category-level object understanding in unconstrained real-world settings.