← 研踪 PaperTrailCVF · CVPR 2026:cvf:125cdbab12ccfd4f71ca1d0ddc956e47 · 记录 #1
SeeGroup: Multi-Layer Depth Estimation of Transparent Surfaces via Self-Determined Grouping
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 02:32
摘要
Transparent objects are common in daily life, and understanding their multi-layer depth information, including both the transparent surface and the objects behind it, is crucial for real-world applications that interact with transparent materials.However, existing depth methods produce only a single depth map, which is inherently ambiguous for transparent surfaces.In this work, We propose a multi-layer depth estimation method, SeeGroup, consisting of novel recurrent decomposition module design and an intensity-based formulation for multi-layer depth. Experiments demonstrate that our method significantly improves the state of the art of multi-layer depth estimation, improving quadruplet relative depth accuracy on LayeredDepth benchmark from 61.34% to 70.09%.
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记录 #1 · 2026 · 最新观测版本
SeeGroup: Multi-Layer Depth Estimation of Transparent Surfaces via Self-Determined Grouping
Transparent objects are common in daily life, and understanding their multi-layer depth information, including both the transparent surface and the objects behind it, is crucial for real-world applications that interact with transparent materials.However, existing depth methods produce only a single depth map, which is inherently ambiguous for transparent surfaces.In this work, We propose a multi-layer depth estimation method, SeeGroup, consisting of novel recurrent decomposition module design and an intensity-based formulation for multi-layer depth. Experiments demonstrate that our method significantly improves the state of the art of multi-layer depth estimation, improving quadruplet relative depth accuracy on LayeredDepth benchmark from 61.34% to 70.09%.
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