ClothAtlas: Joint Garment Identification and State Est, unde
Overview
Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: ClothAtlas: Joint Garment Identification and State Estimation under Self-Occlusion. This paper introduces ClothAtlas, a method for jointly identifying garments and estimating their complete configurations from partial, self-occluded observations. The approach uses a shared graph-conditioned flow-matchin…
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Reported quantity · number of test garments: 22 other · Basis not reported
“raining. On held-out DexGarmentLab garments, ClothAtlas ranks the correct mesh first among 221 candidates for all 22 test garments from a single flat observation. From a single self-occluded observation, it identifies 15/22 garments, compared with 8/22 for a deterministic graph estimator. Persistent vertex identities support grasp-target localization on real garments, reducing mean error from 15.4 to 6.4 cm overall a”
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Reported quantity · number of candidates: 221 other · Basis not reported
“raining. On held-out DexGarmentLab garments, ClothAtlas ranks the correct mesh first among 221 candidates for all 22 test garments from a single flat observation. From a single self-occluded observation, it identifies 15/22 garments, compared with 8/22 for a deterministic graph estimator. Persistent vertex identities support grasp-target localization on real garments, reducing mean error from 15.4 to 6.4 cm overall a”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsClothAtlas: Joint Garment Identification and State Estimation under Self-Occlusion
This paper introduces ClothAtlas, a method for jointly identifying garments and estimating their complete configurations from partial, self-occluded observations. The approach uses a shared graph-conditioned flow-matching model to sample and rank candidate meshes, achieving high accuracy in garment identification and localization, with significant improvements over existing methods in both full and occluded scenarios.
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