A Regional Regression Network for Monocular Object Distance Estimation
Yufeng Zhang, Yuxi Li, Mingbi Zhao, Xiaoyuan Yu · 2020
Monocular pipelines are convenient and cheap solutions for object distance estimation in 3D vision. Current methods for monocular object distance estimation either perform inaccurately or require heavy work. In this paper, we propose a network with R-CNN based structure to implement object detection and distance estimation simultaneously. To improve the estimation results, we add a shallow network to process camera extrinsic parameters and optimize feature processing structure. We train and validate our network on KITTI object dataset, and compare with other methods to show that our method is accurate and easy to train. To prove the generality of our method under other scenarios, we construct a dataset of surveillance scenes, and conduct similar experiments on this dataset.