无需形状先验，实现可变形物体的高精度点云重建
ParCo-SDF: Learning Prior-Free Partial-to-Complete Signed Distance Fields of Deformable Objects
- 分两阶段构建无先验的符号距离场，利用时序结构相似性稳定训练
- 在严重遮挡下仍能实现高保真重建，误差显著低于基线方法
- 适合需要精确操控可变形物体的机器人任务
本研究针对可变形物体（DOs）从点云观测中进行部分到完整的几何重建问题，以实现精准操控。现有方法多采用隐式神经表示（INRs）建模连续表面并捕捉结构变化，但通常依赖特定对象的形状先验，影响泛化能力。为此，我们提出ParCo-SDF，一种两阶段的部分到完整符号距离场（SDF）重建框架：先通过时序几何编码器捕捉可变形物体序列间的结构相似性，实现无先验的稳定训练；再通过FiLM条件化网络预测SDF，保持表达能力的同时降低模型复杂度。我们在橡胶带操作数据集上与当前最优的可变形物体表面重建基线对比，验证了该方法在严重遮挡下的鲁棒性和高保真重建能力。
原文摘要 · Abstract (English)
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.
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