Extrinsic Self-Calibration of the Surround-View System: A Weakly Supervised Approach

Abstract

An SVS usually consists of four wide-angle fisheye cameras mounted around the vehicle to sense the surrounding environment. From the images synchronously captured by all cameras, a top-down surround-view can be synthesized, on the premise that both intrinsics and extrinsics of the cameras have been calibrated. At present, the intrinsic calibration approach is relatively well-developed and can be pipelined, while the extrinsic calibration is still immature. On one hand, the existing manual calibration schemes are usually reliable, but need to be conducted by professionals in specific sites, which is undoubtedly cumbersome. On the other hand, the majority of the existing self- calibration schemes are based on low-level features and their stability and robustness are usually unsatisfactory. As far as we know, an effective extrinsic self-calibration scheme designed specially for the SVS is still lacking. To fill such a research gap to some extent, we propose a novel self-calibration scheme which follows a weakly supervised framework, namely WESNet (Weakly-supervised Extrinsic Self-calibration Network). The training of WESNet consists of two stages. First, we utilize the corners in a few calibration site images as the weak supervision to roughly optimize the network by minimizing the geometric loss. Then, after the convergency in the first stage, we additionally introduce a self-supervised photometric loss term that can be constructed by the photometric information from natural images for further fine-tuning. Besides, to support training, we totally collected 19,078 groups of synchronously captured fisheye images under various environmental conditions. To our knowledge, thus far this is the largest surround-view dataset containing original fisheye images. By means of learning prior knowledge from the training data, WESNet takes the original fisheye images synchronously collected as the input, and directly yields extrinsics end-to-end with little labor cost. Its efficiency and efficacy have been corroborated by extensive experiments conducted on our collected dataset. To make our results reproducible, source code and the collected dataset have been released at https://cslinzhang.github.io/WESNet/WESNet.html.