让模型自适应分层，精准估算透明物体多层深度。
SeeGroup: Multi-Layer Depth Estimation of Transparent Surfaces via Self-Determined Grouping
- 将深度层视为无序事件，避免预设分组限制。
- 在LayeredDepth数据集上相对深度准确率提升至70.09%。
- 适合需要精确透明物体三维结构的视觉任务使用。
透明物体在日常中常见，理解其多层深度（包括透明表面及背后物体）至关重要。现有方法通常扩展单层预测，通过3D点从前到后的顺序定义层数并逐层预测。然而，由于分层几何可能允许多种有效点集分组方式，预设分组策略存在本质局限。本文提出SeeGroup，一种无需预设分组的多层深度估计方法，让模型自主将表面分配至深度图。我们将每像素多层深度建模为点过程，将深度层视为沿每条相机射线的无序事件，从而生成对观察到的深度层具有置换不变性的似然函数，得到可自然支持任意分组的损失函数。实验表明，该方法显著提升多层深度估计性能，在LayeredDepth基准上相对深度准确率从61.34%提升至70.09%。代码已公开于https://github.com/princeton-vl/SeeGroup。
原文摘要 · Abstract (English)
Transparent objects are common in daily life, and it is important to understand their multilayer depth, including the transparent surface and the objects behind it. Existing methods for multilayer depth typically extend single-layer prediction. They define layers by the front-to-back ordering of 3D points and predict the layers sequentially. However, as layered geometry can admit multiple valid groupings of 3D points into layers, a predefined grouping strategy is inherently restrictive. In this work, we propose SeeGroup, a multi-layer depth estimation method that avoids imposing a predefined grouping and 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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