Gated Stereo: Joint Depth Estimation from Gated and Wide-Baseline Active Stereo Cues
This work addresses high-resolution, long-range depth estimation for automotive applications, representing a domain-specific advancement.
The paper tackles long-range depth estimation for automotive scenarios by combining active gated stereo images with multi-view cues, achieving over 50% MAE improvement compared to RGB stereo methods and 74% MAE improvement over monocular gated methods for distances up to 160 m.
We propose Gated Stereo, a high-resolution and long-range depth estimation technique that operates on active gated stereo images. Using active and high dynamic range passive captures, Gated Stereo exploits multi-view cues alongside time-of-flight intensity cues from active gating. To this end, we propose a depth estimation method with a monocular and stereo depth prediction branch which are combined in a final fusion stage. Each block is supervised through a combination of supervised and gated self-supervision losses. To facilitate training and validation, we acquire a long-range synchronized gated stereo dataset for automotive scenarios. We find that the method achieves an improvement of more than 50 % MAE compared to the next best RGB stereo method, and 74 % MAE to existing monocular gated methods for distances up to 160 m. Our code,models and datasets are available here.