Paper Overview
Field: Computer Vision (CV) Authors: Samer Abualhanud, Max Mehltretter Published: 2026-09-04 arXiv: 2609.05397
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.
The paper targets two main sources of cross-image inconsistency:
1. Differences in camera intrinsics — addressed by conditioning the features on per-pixel camera-aware ray embeddings, enabling the network to account for camera-dependent variations in monocular cues. 2. Limited receptive field of each image — addressed by extending each pixel's receptive field (via geometry-constrained attention).
Key Points
- Focus: generalizable multi-view depth estimation for autonomous driving with surround camera rigs.
- Problem: minimal overlap between adjacent camera views means most depth estimates rely on monocular cues, which can be inconsistent across images.
- Solution 1: per-pixel camera-aware ray embeddings to compensate for varying camera intrinsics.
- Solution 2: geometry-constrained attention to extend the receptive field and enforce cross-image consistency.