Abstract
This paper deals with deep neural networks for predicting accurate dense disparity map with Semi-global matching (SGM). SGM is a widely used regularization method for
real scenes because of its high accuracy and fast computation speed. Even though SGM can obtain accurate results, tuning of SGM’s penalty-parameters, which control a
smoothness and discontinuity of a disparity map, is uneasy
and empirical methods have been proposed. We propose a
learning based penalties estimation method, which we call
SGM-Nets that consist of Convolutional Neural Networks.
A small image patch and its position are input into SGMNets to predict the penalties for the 3D object structures.
In order to train the networks, we introduce a novel loss
function which is able to use sparsely annotated disparity
maps such as captured by a LiDAR sensor in real environments. Moreover, we propose a novel SGM parameterization, which deploys different penalties depending on either
positive or negative disparity changes in order to represent
the object structures more discriminatively.
Our SGM-Nets outperformed state of the art accuracy
on KITTI benchmark datasets.