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

Sungil Choi, Seungryong Kim, Kihong Park, Kwanghoon Sohn

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 multi-view stereo. Experiments show that our estimated depth map from multiple views shows the better performance than the other matching similarity measure on DTU dataset.