FisheyeDepth: A Real Scale Self-Supervised Depth Estimation Model for Fisheye Camera
This addresses the problem of accurate depth estimation for robotics and autonomous vehicles using fisheye cameras, which is incremental by building on existing self-supervised methods with specific adaptations.
The paper tackles depth estimation for fisheye cameras by introducing FisheyeDepth, a self-supervised model that incorporates a fisheye camera model and real-scale pose information, resulting in improved accuracy and robustness as demonstrated on public datasets and real-world scenarios.
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.