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

2026-05-28

AI Summary

This work addresses the challenge of partial-to-complete geometric reconstruction of deformable objects under severe occlusion in point cloud observations. The authors propose a two-stage reconstruction framework that requires no object-specific shape priors. The approach first leverages a temporal geometry encoder to capture structural similarities across input sequences, then integrates this information into a FiLM-conditioned implicit signed distance function (SDF) network to achieve high-fidelity surface reconstruction. By fusing temporal cues with a lightweight conditioning mechanism, the method enhances generalization and training stability while preserving expressive power. Experiments on a rubber band manipulation dataset demonstrate that the proposed approach significantly outperforms existing methods, achieving robust and accurate reconstructions even in highly occluded scenarios.

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. 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.

Problem

• deformable objects
• partial-to-complete reconstruction
• signed distance field
• shape prior
• point-cloud

Innovation

• prior-free
• signed distance field
• deformable objects
• temporal geometry encoding
• FiLM conditioning