用扩散模型逐步修复立体匹配中的几何细节，提升边缘清晰度。
StereoDiffuer: Diffusion-based Progressive Geometry Modeling with Saliency Attention Perception for Stereo Matching
- 基于扩散模型迭代优化视差图，逐步恢复被模糊的几何细节。
- 在Scene Flow和KITTI上达到领先性能，显著减少边缘模糊问题。
- 引入显著性注意力模块捕捉边界、细结构等关键几何特征，适合高精度场景重建。
随着深度神经网络的发展，立体匹配生成的视差图质量持续提升。然而，现有方法仍难以保留精细几何细节，导致在复杂区域出现边缘模糊和过度平滑。为此，我们提出StereoDiffuer，一种基于扩散的迭代立体匹配框架，显式建模几何细节并逐步精修视差估计。该框架引入显著性注意力感知（SAP）模块，提取包括物体边界、细长结构和锐利边缘在内的显著几何线索。置信度引导的SAP特征与初始视差估计结合，用于条件化迭代去噪扩散过程，修正残余视差误差，并恢复在代价体积正则化和上采样过程中被抑制的几何细节。在Scene Flow和KITTI基准上的实验结果验证了该框架的有效性，其性能优于对比方法。
原文摘要 · Abstract (English)
With the advance of deep neural networks, the quality of disparity maps obtained through stereo matching has steadily improved. However, existing stereo matching methods still struggle to preserve fine-grained geometric details, resulting in blurred edges and over-smoothed predictions in challenging regions. To address these limitations, we propose StereoDiffuer, an iterative diffusion-based stereo matching framework that explicitly models geometric details and progressively refines disparity estimates. The framework incorporates a Saliency Attention Perception (SAP) module to extract salient geometric cues, including object boundaries, thin structures, and sharp edges. Confidence-guided SAP features are combined with the initial disparity estimate to condition an iterative denoising diffusion process, which corrects residual disparity errors and restores geometric details suppressed during cost-volume regularization and upsampling. Experimental results on the Scene Flow and KITTI benchmarks demonstrate the effectiveness of the proposed framework and its competitive performance relative to the compared stereo matching methods.
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