Generalized Sampling of Non-Local Textural Clues Multi-View Stereo Framework

摘要

3D reconstruction remains a pivotal topic in contemporary multimedia research, with multi-view stereo (MVS) methods being instrumental. Traditional MVS algorithms, have demonstrated robust performance in large-scale scenes. However, these methods often struggle with depth estimation in non-textured regions, resulting in inconsistencies across different areas of the reconstructed 3D model. This paper introduces a novel MVS framework, General Sampling Non-local Texture Features (GST-MVS), designed to enhance depth estimation by leveraging the intrinsic relationship between image texture and object depth. Our approach optimizes the 3D reconstruction process, yielding superior results, especially in low-textured regions. Comprehensive evaluations on the ETH3D dataset indicate that GST-MVS achieves dense and accurate 3D point cloud reconstructions, outperforming existing MVS algorithms. The GST-MVS framework is open-source and accessible on GitHub at https://github.com/Jasmine-tjy/GST-mvs. Additionally, Docker image facilitating the required execution environment, which is available at https://hub.docker.com/r/tangjas111/cmake_cuda_opencv/tags.