Adaptive Fusion of Single-View and Multi-View Depth for Autonomous Driving

Understand

Multi-view depth estimation has achieved impressive performance over various benchmarks.

• However, almost all current multi-view systems rely on given ideal camera poses, which are unavailable in many real-world scenarios, such as autonomous driving.
• In this work, we propose a new robustness benchmark to evaluate the depth estimation system under various noisy pose settings.
• Surprisingly, we find current multi-view depth estimation methods or single-view and multi-view fusion methods will fail when given noisy pose settings.