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Plan3D: Viewpoint and Trajectory Optimization for Aerial Multi-View Stereo Reconstruction

Published:14 December 2018Publication History
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Abstract

We introduce a new method that efficiently computes a set of viewpoints and trajectories for high-quality 3D reconstructions in outdoor environments. Our goal is to automatically explore an unknown area and obtain a complete 3D scan of a region of interest (e.g., a large building). Images from a commodity RGB camera, mounted on an autonomously navigated quadcopter, are fed into a multi-view stereo reconstruction pipeline that produces high-quality results but is computationally expensive. In this setting, the scanning result is constrained by the restricted flight time of quadcopters. To this end, we introduce a novel optimization strategy that respects these constraints by maximizing the information gain from sparsely sampled viewpoints while limiting the total travel distance of the quadcopter. At the core of our method lies a hierarchical volumetric representation that allows the algorithm to distinguish between unknown, free, and occupied space. Furthermore, our information gain-based formulation leverages this representation to handle occlusions in an efficient manner. In addition to the surface geometry, we utilize free-space information to avoid obstacles and determine collision-free flight paths. Our tool can be used to specify the region of interest and to plan trajectories. We demonstrate our method by obtaining a number of compelling 3D reconstructions, and we provide a thorough quantitative evaluation showing improvement over previous state-of-the-art and regular patterns.

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  1. Plan3D: Viewpoint and Trajectory Optimization for Aerial Multi-View Stereo Reconstruction

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                cover image ACM Transactions on Graphics
                ACM Transactions on Graphics  Volume 38, Issue 1
                February 2019
                176 pages
                ISSN:0730-0301
                EISSN:1557-7368
                DOI:10.1145/3300145
                Issue’s Table of Contents

                Copyright © 2018 ACM

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                Publication History

                • Published: 14 December 2018
                • Revised: 1 September 2018
                • Accepted: 1 September 2018
                • Received: 1 April 2018
                Published in tog Volume 38, Issue 1

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