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CrossDepth: Geometry-Constrained Attention for Generalizable Multi-View Surround Depth Estimation — arXiv:2609.05397

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.05397
License (as archived): CC0-1.0
Archived: 2026-10-16T23:41:26.177Z
SHA-256 of the archived text: 2e3effe72ba0e02478fa34f8038699624ab26db9197c2a3fc4b0fa3bd09ec2f6
Stable URL of this page: https://metraipolis.com/documento/dc_2161a73fed65

How to cite

CrossDepth: Geometry-Constrained Attention for Generalizable Multi-View Surround Depth Estimation — arXiv:2609.05397 — arXiv (en), https://arxiv.org/abs/2609.05397 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2161a73fed65 (Metraipolis archive dc_2161a73fed65, sha256 2e3effe72ba0e02478fa34f8038699624ab26db9197c2a3fc4b0fa3bd09ec2f6)

BibTeX

@misc{dc2161a73fed65,
  title = {CrossDepth: Geometry-Constrained Attention for Generalizable Multi-View Surround Depth Estimation — arXiv:2609.05397},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.05397},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2161a73fed65; sha256 2e3effe72ba0e02478fa34f8038699624ab26db9197c2a3fc4b0fa3bd09ec2f6},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.05397. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 this https URL .

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-07 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.05397 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T23:16:11.463Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-07?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).