# αDepth：单次软边界分解实现立体转换

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-29 08:00
- AIHOT 分数：42
- AIHOT 链接：https://aihot.virxact.com/items/cmpxvo61y00hesliwxe3jeggz
- 原文链接：https://arxiv.org/abs/2606.00386

## AI 摘要

αDepth提出一种分层表示方法，将软边界（如毛发、散焦模糊）分解为分层颜色和深度值，以解决立体转换中前景与背景模糊混合导致的深度对应歧义。针对多目标复杂场景，设计圆形Alpha表示（CAR），从全局目标提取转向局部边界分解，无需人工干预即可实现场景级推理。实验表明，αDepth在立体转换中达到最先进水平，消除了软边界处的背景渗色和结构扭曲。

## 正文

Accurately modeling soft boundaries, e.g., hair and defocus blur, is a fundamental challenge in stereo conversion due to the ambiguous blending of foreground and background. Existing depth models primarily predict single-layer depth, leading to ambiguity in depth correspondence at soft boundaries. While matting techniques can capture opacity for layered modeling, they often struggle in complex scenes with multiple targets and usually require user intervention. This paper introduces αDepth, a layered representation that decomposes soft boundaries for high-fidelity stereo conversion. Specifically, we first resolve mixed color and depth ambiguity by estimating layered color and depth values at soft boundaries. Considering complex multi-target scenes, we design a Circular Alpha Representation (CAR) that shifts the paradigm from global target extraction to local boundary decomposition. Unlike prior matting methods restricted to a single foreground/background, CAR enables efficient scene-level inference without manual guidance. Extensive evaluations demonstrate that αDepth achieves state-of-the-art performance in stereo conversion, eliminating background bleeding and structural distortions at soft boundaries.