ArtModeling: An Instance-Matched Dataset and Benchmark for Articulated-Object Perception and Dynamics

Simulation assets are widely used to evaluate articulated-object perception and interaction, but performance measured in simulation does not by itself establish accuracy on physical objects. Controlled comparison requires real observations, detailed digital assets, and recorded motion linked to the same physical instances. We introduce ArtModeling, an instance-matched dataset and benchmark pairing 497 physical objects across 43 categories with articulated digital assets, within a collection of 1,500 simulation-ready models. Each paired instance combines calibrated multi-state RGB-D observations, camera poses, part-level masks, an articulated digital asset, and recorded interaction motion. The assets are manually modeled from independent scans and physical measurements, capturing detailed part geometry and occluded structures. Registration to the metric frame of the real observations enables evaluation on real and rendered inputs with matched viewpoints and joint states against a shared geometric reference. The benchmark evaluates articulation estimation from real observations, performance differences under matched real and rendered inputs, and physical consistency on held-out interactions. Across the evaluated reconstruction methods, geometric rankings remain relatively stable across input domains, while joint-estimation rankings change, with larger discrepancies for methods using RGB-D inputs. Motion-based calibration improves trajectory agreement on held-out interactions. ArtModeling provides a reusable basis for assessing how simulation findings extend to real articulated objects, enabling controlled evaluation of articulated perception and dynamics against shared physical references.