Monocular Fisheye Camera Depth Estimation Using Sparse LiDAR Supervision

TL;DR

This paper presents a novel method for depth estimation from monocular fisheye cameras using sparse LiDAR data for supervision, addressing the limitations of synthetic datasets and domain shift in real-world autonomous driving scenarios.

Contribution

It introduces a new dataset and a training approach that leverages sparse LiDAR data as ground truth for fisheye camera depth estimation, with improved network architecture.

Findings

Achieved RMSE errors comparable to state-of-the-art on KITTI.

Developed occlusion resolution for view point differences.

Demonstrated effective depth estimation using sparse LiDAR supervision.

Abstract

Near field depth estimation around a self driving car is an important function that can be achieved by four wide angle fisheye cameras having a field of view of over 180. Depth estimation based on convolutional neural networks (CNNs) produce state of the art results, but progress is hindered because depth annotation cannot be obtained manually. Synthetic datasets are commonly used but they have limitations. For instance, they do not capture the extensive variability in the appearance of objects like vehicles present in real datasets. There is also a domain shift while performing inference on natural images illustrated by many attempts to handle the domain adaptation explicitly. In this work, we explore an alternate approach of training using sparse LiDAR data as ground truth for depth estimation for fisheye camera. We built our own dataset using our self driving car setup which has a 64 beam Velodyne LiDAR and four wide angle fisheye cameras. To handle the difference in view points of LiDAR and fisheye camera, an occlusion resolution mechanism was implemented. We started with Eigen's multiscale convolutional network architecture and improved by modifying activation function and optimizer. We obtained promising results on our dataset with RMSE errors comparable to the state of the art results obtained on KITTI.