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CrossDepth: Geometry-Constrained Attention for Generalizable Multi-View Surround Depth Estimation

Paper recorded by Signals 4 on 2026-09-04 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-04 on arXiv · recorded by Signals 4 on 2026-09-07

Category: cs.CV · 计算机视觉 · first seen 2026-09-07

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 interprete

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#131 most recent of 237 cs.CV papers we have recorded · ↑ newer: From Interpretability Methods to Interpretable Models · ↓ older: Think-Verify-Revise: Neuro-Symbolic Visual Reasoning with Vision-Langu
Cite this page: CrossDepth: Geometry-Constrained Attention for Generalizable Multi-View Surround Depth Estimation: the #131 most recent of 237 cs.CV papers we have recorded (as of 2026-09-04). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/crossdepth-geometry-constrained-attention-for-generalizable-multi-view-surround-.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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