针对鱼眼相机的深度估计，提出自监督模型FisheyeDepth，提升精度与稳定性。
FisheyeDepth: A Real Scale Self-Supervised Depth Estimation Model for Fisheye Camera
- 将鱼眼相机模型融入投影与重投影过程，处理图像畸变。
- 用真实尺度位姿替代传统位姿网络，提升深度物理准确性。
- 多通道输出融合多尺度特征，增强对真实位姿噪声的鲁棒性。
准确的深度估计对机器人和自动驾驶中的三维场景理解至关重要。鱼眼相机虽具有宽视场优势，但其深度估计受限于真实标签数据稀缺及图像畸变问题。本文提出FisheyeDepth，一种专为鱼眼相机设计的自监督深度估计模型。通过在训练中引入鱼眼相机模型，优化投影与重投影阶段，有效缓解畸变影响，提升深度估计精度与训练稳定性。此外，采用真实尺度位姿信息替代传统位姿网络估计值，确保深度具备实际物理意义，同时简化训练与推理流程。我们还设计了多通道输出策略，通过自适应融合多尺度特征，降低真实位姿数据带来的噪声影响。实验在公开数据集与真实场景中均验证了模型在鱼眼图像深度估计上的优越性能与鲁棒性。项目主页见：https://github.com/guoyangzhao/FisheyeDepth。
原文摘要 · Abstract (English)
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.
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