发丝守护者：深度、立体与新视角中柔韧边界的拯救

摘要

软边界（如发丝）在自然图像和计算机生成图像中普遍存在，但由于前景与背景线索的模糊混合，它们始终是三维视觉领域的挑战。本文提出发丝守护者框架，该框架专为恢复三维视觉任务中的细粒度软边界细节而设计。具体而言，我们首先提出一种利用图像抠图数据集进行训练的新型数据构建流程，并设计深度修复网络自动识别软边界区域。通过门控残差模块，该网络能在保持全局深度质量的同时精确优化软边界周围的深度，实现与前沿深度模型的即插即用集成。在视图合成方面，我们采用基于深度的前向扭曲以保留高保真纹理，随后通过生成式场景绘制器填充遮挡解除区域并消除软边界内的冗余背景伪影。最终，色彩融合模块自适应地结合扭曲与修复结果，生成具有一致几何结构和细粒度细节的新视图。大量实验表明，HairGuard在单目深度估计、立体图像/视频转换及新视图合成任务中均达到最先进性能，尤其在软边界区域实现显著提升。

English

Soft boundaries, like thin hairs, are commonly observed in natural and computer-generated imagery, but they remain challenging for 3D vision due to the ambiguous mixing of foreground and background cues. This paper introduces Guardians of the Hair (HairGuard), a framework designed to recover fine-grained soft boundary details in 3D vision tasks. Specifically, we first propose a novel data curation pipeline that leverages image matting datasets for training and design a depth fixer network to automatically identify soft boundary regions. With a gated residual module, the depth fixer refines depth precisely around soft boundaries while maintaining global depth quality, allowing plug-and-play integration with state-of-the-art depth models. For view synthesis, we perform depth-based forward warping to retain high-fidelity textures, followed by a generative scene painter that fills disoccluded regions and eliminates redundant background artifacts within soft boundaries. Finally, a color fuser adaptively combines warped and inpainted results to produce novel views with consistent geometry and fine-grained details. Extensive experiments demonstrate that HairGuard achieves state-of-the-art performance across monocular depth estimation, stereo image/video conversion, and novel view synthesis, with significant improvements in soft boundary regions.

发丝守护者：深度、立体与新视角中柔韧边界的拯救

Guardians of the Hair: Rescuing Soft Boundaries in Depth, Stereo, and Novel Views

摘要

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