FisheyeDepth: A Real Scale Self-Supervised Depth Estimation Model for Fisheye Camera

TL;DR

FisheyeDepth is a self-supervised model designed for accurate, real-scale depth estimation from fisheye camera images, incorporating fisheye geometry and pose information to improve robustness and training stability.

Contribution

The paper introduces a novel self-supervised depth estimation approach specifically for fisheye cameras, integrating fisheye geometry and real-scale pose data to enhance accuracy and robustness.

Findings

Outperforms existing methods on public datasets

Provides real-scale depth estimates suitable for robotic applications

Demonstrates robustness in real-world scenarios

Abstract

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.