Learning Descriptor, Confidence, and Depth Estimation in Multi-view Stereo

Abstract

Depth estimation from multi-view stereo images is one of the most fundamental and essential tasks in understanding a scene imaginary. In this paper, we propose a machine learning technique based on deep convolutional neural networks (CNNs) for multi-view stereo matching. The proposed method measures the matching cost to extract depth values between two-view stereo images among multi-view stereo images using a deep architecture. Moreover, we present the confidence estimation network for incorporating the cost volumes along the depth hypothesis in multiview stereo. Experiments show that our estimated depth map from multiple views shows the better performance than the other matching similarity measure on DTU dataset.