Weakly Supervised Monocular Fisheye Camera Distance Estimation with Segmentation Constraints

Monocular fisheye camera distance estimation is a crucial visual perception task for autonomous driving. Due to the practical challenges of acquiring precise depth annotations, existing self-supervised methods usually consist of a monocular distance model and an ego-motion predictor with the goal of minimizing a reconstruction matching loss. However, they suffer from inaccurate distance estimation in low-texture regions, especially road surfaces. In this paper, we introduce a weakly supervised learning strategy that incorporates semantic segmentation, instance segmentation, and optical flow as additional sources of supervision. In addition to the self-supervised reconstruction loss, we introduce a road surface flatness loss, an instance smoothness loss, and an optical flow loss to enhance the accuracy of distance estimation. We evaluate the proposed method on the WoodScape and SynWoodScape datasets, and it outperforms the self-supervised monocular baseline, FisheyeDistanceNet.