AI 中文总结
研究提出WAVE-Stereo立体匹配方法，基于相关体与特征扭曲互补线索，通过地理扭曲对应编码器并行编码多信息，用周期性全局上下文传播减轻无纹理区匹配退化，在多基准测试中实现精度与效率良好平衡。
AI 中文摘要
现有的迭代立体匹配方法主要采用两种对应表示：通过相关体进行显式匹配搜索和通过扭曲特征进行局部残差细化，但两者仍分别建模。我们提出了WAVE-Stereo，基于相关体和特征扭曲提供互补匹配线索这一核心见解。地理扭曲对应编码器（GWCE）在ConvGRU输入时并行编码匹配搜索、残差对齐和视差先验。为减轻无纹理区域的匹配退化，提出周期性全局上下文传播（PGCP）。在五个真实世界基准测试中，WAVE-Stereo在无外部基础模型先验的情况下实现了有竞争力的零样本泛化精度，在KITTI 2015上达到3.18%的D1-all，在Booster上达到4.42%的Bad-2.0，实时推理为66毫秒，在精度和效率之间取得了良好平衡。
英文摘要
Existing iterative stereo matching methods primarily adopt two types of correspondence representation: explicit matching search via correlation volumes and local residual refinement via warped features, yet the two remain separately modeled. We propose WAVE-Stereo, built on a core insight: correlation volumes and feature warping provide complementary matching cues. \textbf{GeoWarp Correspondence Encoder (GWCE)} encodes matching search, residual alignment, and disparity prior in parallel at the ConvGRU input. To mitigate matching degradation in textureless regions, we propose \textbf{Periodic Global Context Propagation (PGCP)}, which propagates global spatial information in a periodic manner. On five real-world benchmarks -- Middlebury, ETH3D, KITTI 2012, KITTI 2015, and Booster -- WAVE-Stereo achieves competitive zero-shot generalization accuracy without any external foundation model prior, achieving 3.18\% D1-all on KITTI 2015, 4.42\% Bad-2.0 on Booster, and 66ms real-time inference, striking a favorable balance between accuracy and efficiency. Our code is available at https://github.com/yamanoko-do/WAVE-Stereo.
