FisheyeDepth：魚眼カメラ向け実スケール自己教師あり深度推定モデル
FisheyeDepth: A Real Scale Self-Supervised Depth Estimation Model for Fisheye Camera
魚眼カメラの歪みを考慮した投影・再投影と実スケールの姿勢情報を組み合わせ、自己教師ありで実用的な深度推定を可能にしたモデルを提案。
著者: Guoyang Zhao, Yuxuan Liu, Weiqing Qi, Fulong Ma, Ming Liu, Jun Ma
分類: cs.CV, cs.RO
原文アブストラクト
Accurate depth estimation is crucial for 3D scene comprehension in robotics and autonomous vehicles. Fisheye cameras, known for their wide field of view, have inherent geometric benefits. However, their use in depth estimation is restricted by a scarcity of ground truth data and image distortions. We present FisheyeDepth, a self-supervised depth estimation model tailored for fisheye cameras. We incorporate a fisheye camera model into the projection and reprojection stages during training to handle image distortions, thereby improving depth estimation accuracy and training stability. Furthermore, we incorporate real-scale pose information into the geometric projection between consecutive frames, replacing the poses estimated by the conventional pose network. Essentially, this method offers the necessary physical depth for robotic tasks, and also streamlines the training and inference procedures. Additionally, we devise a multi-channel output strategy to improve robustness by adaptively fusing features at various scales, which reduces the noise from real pose data. We demonstrate the superior performance and robustness of our model in fisheye image depth estimation through evaluations on public datasets and real-world scenarios. The project website is available at: https://github.com/guoyangzhao/FisheyeDepth.
