Paper Overview
Field: Computer Vision (CV) Authors: Samer Abualhanud, Max Mehltretter Posted: 2026-09-04 arXiv: 2609.05397
Original Abstract (partial, as collected)
> 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... *(truncated in source)*
Key Points
- Problem: Multi-view surround camera rigs in autonomous driving have minimal overlap between adjacent images, so most pixel depths must rely on monocular appearance cues that vary across images.
- Challenge 1 — camera intrinsics differences: Handled by conditioning features on per-pixel camera-aware ray embeddings, letting the network account for camera-dependent variation in monocular cues.
- Challenge 2 — limited receptive field: Addressed by extending each pixel's receptive field (details truncated in the collected abstract; see the full paper on arXiv).
*Auto-collected on 2026-09-08. Chinese translation pending.*