SeeGroup
we propose SeeGroup, a multi-layer depth estimation method that allows the model itself to adaptively assign surfaces to depth maps. We formulate per-pixel multi-layer depth as a point process, treating depth layers...
Fuente del modelo
- Tipo
- Other
- Tamaño del archivo
- 3,97 GB
- Versión
- vVersion 1
- Actualizado
- 10 oct 2026
- Fuente
- Hugging Face
Descripción de la fuente
[CVPR 2026] SeeGroup: Multi-Layer Depth Estimation of Transparent Surfaces via Self-Determined Grouping
we propose SeeGroup, a multi-layer depth estimation method that allows the model itself to adaptively assign surfaces to depth maps. We formulate per-pixel multi-layer depth as a point process, treating depth layers as unordered events along each camera ray. This induces a permutation-invariant likelihood over the observed depth layers, yielding a loss that naturally supports arbitrary layer groupings. Experiments demonstrate that our method significantly advances the state of the art of multi-layer depth estimation, improving quadruplet relative depth accuracy on LayeredDepth benchmark from 61.34% to 70.09%. Code is available at https://github.com/princeton-vl/SeeGroup.
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 10 oct
Artefactos del modelo
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Artefactos del modelo
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Extractos de fuentes
2 extractos--- license: bsd-3-clause pipeline_tag: depth-estimation --- # [CVPR 2026] SeeGroup: Multi-Layer Depth Estimation of Transparent Surfaces via Self-Determined Grouping we propose SeeGroup, a multi-layer depth estimation method that allows the model itself to adaptively assign surfaces to depth maps. We formulate per-pixel multi-layer depth as a point process, treating depth layers as unordered events along each camera ray. This induces a permutation-invariant likelihood over the observed depth layers, yielding a loss that naturally supports arbitrary layer groupings. Experiments demonstrate that our method significantly advances the state of the art of multi-layer depth estimation, improving quadruplet relative depth accuracy on LayeredDepth benchmark from 61.34% to 70.09%. Code is available at https://github.com/princeton-vl/SeeGroup.
Source context: 0 downloads · 0 likes · Pipeline depth-estimation · Repo Hermera/SeeGroup
