S2MDF: A Plug-And-Play Layer for Intersection-Free Multi-Object Signed Distance Fields
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911727428304896 |
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| author | Mercadier, Deniz Sayin Stella, Federico Bizeau, Aurel Talabot, Nicolas Fua, Pascal |
| author_facet | Mercadier, Deniz Sayin Stella, Federico Bizeau, Aurel Talabot, Nicolas Fua, Pascal |
| contents | 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 interpenetrate, violating physical plausibility. Existing mitigation strategies rely on soft penalty terms that reduce but do not eliminate intersections, and require careful loss weighting. To truly prevent interpenetration, we propose a hard constraint on vector-valued SDFs and introduce S2MDF, a lightweight plug-and-play module that enforces the constraint on any object-compositional SDF representation without architectural modifications. It introduces negligible computational overhead and is compatible with linearly-interpolated standard meshing algorithms such as Marching Cubes. It can be applied during training or as a post-processing step. Experiments on multiple state-of-the-art compositional methods show that S2MDF reduces intersections to numerical precision while preserving reconstruction quality, outperforming existing mitigation strategies. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_29761 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | S2MDF: A Plug-And-Play Layer for Intersection-Free Multi-Object Signed Distance Fields Mercadier, Deniz Sayin Stella, Federico Bizeau, Aurel Talabot, Nicolas Fua, Pascal Computer Vision and Pattern Recognition Computational Geometry 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 interpenetrate, violating physical plausibility. Existing mitigation strategies rely on soft penalty terms that reduce but do not eliminate intersections, and require careful loss weighting. To truly prevent interpenetration, we propose a hard constraint on vector-valued SDFs and introduce S2MDF, a lightweight plug-and-play module that enforces the constraint on any object-compositional SDF representation without architectural modifications. It introduces negligible computational overhead and is compatible with linearly-interpolated standard meshing algorithms such as Marching Cubes. It can be applied during training or as a post-processing step. Experiments on multiple state-of-the-art compositional methods show that S2MDF reduces intersections to numerical precision while preserving reconstruction quality, outperforming existing mitigation strategies. |
| title | S2MDF: A Plug-And-Play Layer for Intersection-Free Multi-Object Signed Distance Fields |
| topic | Computer Vision and Pattern Recognition Computational Geometry |
| url | https://arxiv.org/abs/2605.29761 |