柱体深度：面向多视角一致自监督环视深度估计的柱形空间注意力机制

摘要

自监督环视深度估计技术能够通过多张最小重叠度的图像，实现360度视场内密集、低成本的3D感知。然而现有方法普遍存在重叠区域深度估计不一致的问题。针对这一局限，我们提出一种面向标定化时间同步多相机阵列的几何引导新方法，可预测稠密公制深度。我们的方法主要解决两个不一致性来源：单图像深度估计在边界区域的有限感受野，以及跨视图对应点匹配的困难。通过扩展跨视图感受野并将跨视图注意力限制在局部邻域，我们有效缓解了这两个问题。具体而言，我们通过将图像特定特征点映射至共享圆柱面来建立图像间的邻域关系，基于圆柱坐标应用具有非学习权重的显式空间注意力机制，根据特征点在圆柱面上的距离进行跨图像特征聚合。调制后的特征最终被解码为各视角的深度图。在DDAD和nuScenes数据集上的评估表明，相较于现有先进方法，本方案在跨视图深度一致性和整体深度精度方面均有提升。代码详见https://abualhanud.github.io/CylinderDepthPage。

English

Self-supervised surround-view depth estimation enables dense, low-cost 3D perception with a 360° field of view from multiple minimally overlapping images. Yet, most existing methods suffer from depth estimates that are inconsistent across overlapping images. To address this limitation, we propose a novel geometry-guided method for calibrated, time-synchronized multi-camera rigs that predicts dense metric depth. Our approach targets two main sources of inconsistency: the limited receptive field in border regions of single-image depth estimation, and the difficulty of correspondence matching. We mitigate these two issues by extending the receptive field across views and restricting cross-view attention to a small neighborhood. To this end, we establish the neighborhood relationships between images by mapping the image-specific feature positions onto a shared cylinder. Based on the cylindrical positions, we apply an explicit spatial attention mechanism, with non-learned weighting, that aggregates features across images according to their distances on the cylinder. The modulated features are then decoded into a depth map for each view. Evaluated on the DDAD and nuScenes datasets, our method improves both cross-view depth consistency and overall depth accuracy compared with state-of-the-art approaches. Code is available at https://abualhanud.github.io/CylinderDepthPage.

柱体深度：面向多视角一致自监督环视深度估计的柱形空间注意力机制

CylinderDepth: Cylindrical Spatial Attention for Multi-View Consistent Self-Supervised Surround Depth Estimation

摘要

Support