Scalable point cloud meshing for image-based large-scale 3D modeling

Jiali Han,Shuhan Shen

doi:10.1186/s42492-019-0020-y

Abstract

Image-based 3D modeling is an effective method for reconstructing large-scale scenes, especially city-level scenarios. In the image-based modeling pipeline, obtaining a watertight mesh model from a noisy multi-view stereo point cloud is a key step toward ensuring model quality. However, some state-of-the-art methods rely on the global Delaunay-based optimization formed by all the points and cameras; thus, they encounter scaling problems when dealing with large scenes. To circumvent these limitations, this study proposes a scalable point-cloud meshing approach to aid the reconstruction of city-scale scenes with minimal time consumption and memory usage. Firstly, the entire scene is divided along the x and y axes into several overlapping chunks so that each chunk can satisfy the memory limit. Then, the Delaunay-based optimization is performed to extract meshes for each chunk in parallel. Finally, the local meshes are merged together by resolving local inconsistencies in the overlapping areas between the chunks. We test the proposed method on three city-scale scenes with hundreds of millions of points and thousands of images, and demonstrate its scalability, accuracy, and completeness, compared with the state-of-the-art methods.

Highlights

3D modeling of large-scale scenes has attracted extensive attention in recent years
Noise and outliers are unavoidably included in the multi-view stereo (MVS) point cloud
To circumvent the limitations of current state-of-theart methods, we propose a scalable point-cloud meshing approach that can efficiently process city-scale scenes based on MVS points with minimal memory usage

Summary

Introduction

3D modeling of large-scale scenes has attracted extensive attention in recent years. It can be applied in many ways such as virtual reality, urban reconstruction, and cultural heritage protection. There are many techniques for obtaining the point cloud of large scenes; the laser-scanner-based and image-based methods appear to be the most widely used. The image-based method takes multi-view images as the input, and produce per-pixel dense point clouds using the structure-from-motion (SfM) and multi-view stereo (MVS) algorithms [4,5,6,7]. For city-scale scene reconstructions, the image-based modeling approach is more convenient and cost-effective, because of the rapid developments of drones and oblique photography.

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Conclusion