ParCo-SDF: 変形物体の事前知識なし部分から完全への符号付き距離場学習
ParCo-SDF: Learning Prior-Free Partial-to-Complete Signed Distance Fields of Deformable Objects
点群観測から変形物体の完全な形状を再構成するため、時間的幾何エンコーディングとFiLM条件付きSDF予測からなる2段階フレームワークを提案した。物体固有の形状事前知識を必要とせず、重度のオクルージョン下でも高忠実な再構成を実現する。
著者: Deokmin Hwang, Minseok Song, Daehyung Park
分類: cs.CV
原文アブストラクト
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. To figure it out, we introduce ParCo-SDF, a two-stage partial-to-complete signed distance field (SDF) reconstruction framework consisting of temporal geometry encoding followed by FiLM-conditioned SDF prediction. The temporal encoder captures structural similarity across DO sequence, enabling prior-free stable training. FiLM-based conditioning preserves reconstruction expressivity while reducing network complexity. We evaluate the proposed method against a state-of-the-art DO surface reconstruction baseline on a rubber band manipulation dataset, demonstrating robust and high-fidelity reconstruction under severe occlusions.