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

新方法解决了可变形和多物体场景的三维几何重建问题

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-29 04:00

两篇新的研究论文介绍了从点云数据重建三维几何的新方法。ParCo-SDF 专注于可变形物体，通过编码时间几何相似性来实现无先验重建。S2MDF 通过引入一个即插即用的层来解决多物体场景表示问题，该层强制执行带符号距离场 (SDF) 的无交集约束，从而提高物理合理性。 AI

影响这些论文推动了三维重建技术的发展，通过实现更准确和物理上更合理的物体和场景建模，有可能改进机器人和虚拟现实中的应用。

排序理由两篇在 arXiv 上发表的学术论文，介绍了三维几何重建的新方法。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.CV TIER_1 English(EN) · Deokmin Hwang, Minseok Song, Daehyung Park · 2026-05-29 04:00

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

arXiv:2605.29417v1 Announce Type: new 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 represent…
arXiv cs.CV TIER_1 English(EN) · Deniz Sayin Mercadier, Federico Stella, Aurel Bizeau, Nicolas Talabot, Pascal Fua · 2026-05-29 04:00

S2MDF: A Plug-And-Play Layer for Intersection-Free Multi-Object Signed Distance Fields

arXiv:2605.29761v1 Announce Type: new Abstract: Compositional implicit surface representations model scenes as collections of objects, each encoded by a Signed Distance Field (SDF). A fundamental limitation of this approach is that multiple SDFs can produce geometries that interp…