A unified method for improving long-range accuracy of stereo and monocular depth estimation algorithms

Environment perception for driving applications requires very accurate sensors especially when dealing with depth measurements. LiDAR is the most trustworthy sensor in this domain, but it suffers from disadvantages in terms of number of scene points and their temporal alignment. These issues are especially relevant when dealing with long-range measurements, where each 3D point is crucial. As an alternative, in this work we focus on camera-based depth perception for objects at large distance by using stereo reconstruction and monocular depth estimation. Towards improving the capabilities of camera-based, we initially introduce a taxonomy to categorize all types of camera-based depth perception methods with respect to their long-range capabilities. We then present a correction method that works for both stereo and monocular depth perception algorithms that output a depth in discrete setting (most suitable for realtime applications). We show that our method improves the precision for such algorithms for objects at large distances without affecting the near-range accuracy. The method requires only several additional operations, preserving the real-time capabilities of the underlying algorithms.