DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction

Abstract

Deep Neural Networks (DNNs) have the potential to improve the quality of image-based 3D reconstructions. A challenge which still remains is to utilize the potential of DNNs to improve 3D reconstructions from high-resolution image datasets as available by the ETH3D benchmark. In this paper, we propose a way to employ DNNs in the image domain to gain a significant quality improvement of geometric image based 3D reconstruction. This is achieved by utilizing confidence prediction networks which have been adapted to the Multi-View Stereo (MVS) case and are trained on automatically generated ground truth established by geometric error propagation. In addition to a semi-dense real-world ground truth dataset for training the DNN, we present a synthetic dataset to enlarge the training dataset. We demonstrate the utility of the confidence predictions for two essential steps within a 3D reconstruction pipeline: Firstly, to be used for outlier clustering and filtering and secondly to be used within a depth refinement step. The presented 3D reconstruction pipeline DeepC-MVS makes use of deep learning for an essential part in MVS from high-resolution images and the experimental evaluation on popular benchmarks demonstrates the achieved state-of-the-art quality in 3D reconstruction.