Category-Level Articulated Object Pose Estimation via Pose–Shape Hypothesis Generation and Verification

Category-level 6D pose estimation of articulated objects froma single-view input is a fundamental yet challenging task. Its core dif-ficulty lies in occlusion-induced pose and shape ambiguities, which arefurther exacerbated by highly entangled pose-shape representations andreal-world depth noise. Existing generative methods often neglect theinherent correlations between pose and shape, resulting in geometricallyinconsistent or physically implausible predictions. To address this limi-tation, we propose a novel generation-and-verification framework for thistask: First, a Pose-Shape Hypothesis Generator jointly models the dis-tributions of pose and shape to generate intrinsically coupled hypothesispairs, providing explicitly paired candidates for subsequent verification.Second, a Part-Level Point Cloud Denoising module recovers clean pointclouds from noisy and partial depth observations, ensuring robust inputsfor the verification stage. Finally, a Cross-Space Consistency Verifica-tion module explicitly verifies the geometric consistency between the ob-served point clouds and canonical shapes using a continuous SDF-basedscoring mechanism, which effectively prunes geometrically implausiblehypotheses. Extensive experiments on challenging benchmarks demon-strate that our method significantly improves the robustness and accu-racy of pose estimation in complex, heavily occluded scenarios. Projectpage: https://github.com/KaifengT/ArtPS.