Active 3D Modeling via Online Multi-View Stereo

Abstract

Multi-view stereo (MVS) algorithms have been commonly used to model large-scale structures. When processing MVS, image acquisition is an important issue because its reconstruction quality depends heavily on the acquired images. Recently, an explore-then-exploit strategy has been used to acquire images for MVS. This method first constructs a coarse model by exploring an entire scene using a pre-allocated camera trajectory. Then, it rescans the unreconstructed regions from the coarse model. However, this strategy is inefficient because of the frequent overlap of the initial and rescanning trajectories. Furthermore, given the complete coverage of images, MVS algorithms do not guarantee an accurate reconstruction result. In this study, we propose a novel view path-planning method based on an online MVS system. This method aims to incrementally construct the target three-dimensional (3D) model in real time. View paths are continually planned based on online feedbacks from the partially constructed model. The obtained paths fully cover low-quality surfaces while maximizing the reconstruction performance of MVS. Experimental results demonstrate that the proposed method can construct high quality 3D models with one exploration trial, without any rescanning trial as in the explore-then-exploit method.