ParCo-SDF

ParCo-SDF: Learning Prior-Free Partial-to-Complete Signed Distance Fields of Deformable Objects

arXiv:2605.29417v2 Announce Type: replace Abstract: This study addresses the partial-to-complete geometry reconstruction of deformable objects (DOs) from point-cloud observations toward precise DO manipulation. Recent DO reconstruction approaches often adopt implicit neural representations (INRs) to model continuous surfaces as well as capture structural variability. However, these methods typically rely on object-specific shape priors that improve training stability and limit generalization.