Problem
Research questions and friction points this paper is trying to address.
multi-view
depth estimation
cross-image inconsistency
camera intrinsics
receptive field
CrossDepth: Geometry-Constrained Attention for Generalizable Multi-View Surround Depth Estimation
🤖 AI Summary
为解决多视角环绕深度估计中跨图像不一致问题，通过几何约束注意力机制和相机感知光线嵌入方法提高深度估计准确性。
📝 Abstract
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 pixel's context beyond its own image through cross-image attention constrained to geometrically plausible regions, derived from the calibrated rig setup. The model is trained in a fully self-supervised manner based on photometric consistency. Evaluations on DDAD and nuScenes show improved overall depth accuracy and cross-image depth consistency over state-of-the-art self-supervised methods under in-domain and cross-domain evaluation. Code is available at https://abualhanud.github.io/CrossDepthPage/.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Geometry-Constrained Attention
Camera-Aware Ray Embeddings
Cross-Image Consistency
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