[论文] CrossDepth: Geometry-Constrained Attention for Generalizable Multi-Vie...

论文概要

研究领域: CV 作者: Samer Abualhanud, Max Mehltretter 发布时间: 2026-09-04 arXiv: 2609.05397

中文摘要

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原文摘要

Reliable 3D understanding of the surrounding environment is a core requirement for autonomous driving. Multi-view surround camera rigs provide broad scene coverage, but the spatially adjacent images typically overlap only minimally. Consequently, the depth of most pixels must be inferred from monocular appearance cues. These cues can appear differently across images and may therefore be interpreted differently by the depth estimation model. We target two main sources of cross-image inconsistency: differences in camera intrinsics and the limited receptive field of each image. We address the former by conditioning the features on per-pixel camera-aware ray embeddings, enabling the network to account for camera-dependent variations in monocular cues. We address the latter by extending each pi...

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