Depth Estimation Based on Fisheye Cameras

Le résumé fourni par la source

Fisheye cameras, with their ultra-wide field of view, offer significant benefits for depth estimation in applications such as autonomous navigation, robotics, and immersive imaging by capturing more scene content from a single viewpoint. However, their strong radial distortion and varying spatial resolution across the image pose substantial challenges for accurate depth prediction. We present a deep learning–based framework for fisheye depth estimation that addresses these challenges while leveraging the wide coverage advantage. During training, rectified and synchronized stereo image pairs are used, with the right image and an estimated initial depth map reconstructing the left image. A refined spatial consistency loss is formulated by combining Structural Similarity Index Measure (SSIM) and L1 loss, with gradient-based weighting to emphasize disparity edges. To overcome the limitations of photometric loss in disparity learning, we normalize pixel intensities to better correlate disparity with appearance features. A fisheye-specific depth refinement module incorporates an uncertainty map derived from an inconsistency mask and a distortion distribution map, mitigating the effects of occlusion and high-distortion regions. This uncertainty map is used to weight the temporal warping loss, enhancing robustness against distortion-prone areas. During inference, only a single fisheye image is required to produce an accurate depth map. Experimental results demonstrate that our method improves reconstruction fidelity and robustness, making it well-suited for real-world fisheye-based depth estimation tasks.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.