English(EN) EMCStereo: Attention-Enhanced Stereo Matching for Thin-Structure Depth Estimation with a Synthetic Tree-Branch Benchmark

新的EMCStereo方法增强了薄结构的深度估计

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了EMCStereo，一种新颖的立体匹配方法，旨在改进树枝等薄结构的深度估计。该方法将三个轻量级注意力模块——高效多尺度注意力（EMA）、多尺度融合块（MSFblock）和坐标注意力（CoordAtt）——集成到一个PSMNet风格的骨干网络中，从而使模型仅略微增大且推理时间开销极小。为了评估EMCStereo，使用Unreal Engine 5创建了一个名为VirtualTree的合成数据集，其中包含薄树枝的精确视差标签。该模型在VirtualTree以及KITTI 2012、KITTI 2015、ETH3D和Middlebury等多个既有基准上均取得了强劲的表现。 AI

影响改进了对具有挑战性的薄结构的深度估计，可能使机器人和自主系统受益。

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arXiv cs.CV TIER_1 English(EN) · Yida Lin, Bing Xue, Mengjie Zhang, Sam Schofield, Richard Green · 2026-09-15 04:00

EMCStereo：用于薄结构深度估计的注意力增强立体匹配及合成树枝基准

arXiv:2609.13233v1 Announce Type: new Abstract: Thin structures such as tree branches are among the hardest cases for stereo matching: a branch is only a few pixels wide, the background is cluttered, and dense ground truth for real branches is nearly impossible to label by hand. …

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EMCStereo：用于薄结构深度估计的注意力增强立体匹配及合成树枝基准

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