Divide and conquer: A hierarchical approach to large-scale structure-from-motion

Bhowmick, Brojeshwar and Patra, Suvam and Chatterjee, Avishek and Govindu, Venu Madhav and Banerjee, Subhashis (2017) Divide and conquer: A hierarchical approach to large-scale structure-from-motion. In: COMPUTER VISION AND IMAGE UNDERSTANDING, 157 (SI). pp. 190-205.

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Abstract

In this paper we present a novel pipeline for large-scale SfM. We first organise the images into a hierarchical tree built using agglomerative clustering. The SfM problem is then solved by reconstructing smaller image sets and merging them into a common frame of reference as we move up the tree in a bottom-up fashion. Such an approach drastically reduces the computational load for matching image pairs without sacrificing accuracy. It also makes the resulting sequence of bundle adjustment problems well-conditioned at all stages of reconstruction. We use motion averaging followed by global bundle adjustment for reconstruction of each individual cluster. Our 3D registration or alignment of partial reconstructions based on epipolar relationships is both robust and reliable and works well even when the available camera-point relationships are poorly conditioned. The overall result is a robust, accurate and efficient pipeline for large-scale SfM. We present extensive results that demonstrate these attributes of our pipeline on a number of large-scale, real-world datasets and compare with the state-of-the-art. (C) 2017 Elsevier Inc. All rights reserved.

Item Type:	Journal Article
Publication:	COMPUTER VISION AND IMAGE UNDERSTANDING
Publisher:	ACADEMIC PRESS INC ELSEVIER SCIENCE, 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA
Additional Information:	Copy right for this article belongs to the ACADEMIC PRESS INC ELSEVIER SCIENCE, 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	20 May 2017 04:35
Last Modified:	20 May 2017 04:35
URI:	http://eprints.iisc.ac.in/id/eprint/56864

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