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...
Fonte do modelo
- Tipo
- Other
- Tamanho do arquivo
- 3,97 GB
- Versão
- vVersion 1
- Atualizado
- 10 de out. de 2026
- Fonte
- Hugging Face
Descrição da fonte
[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.
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1 fonteVerificado 10 de out.
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2 trechos--- 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
