Abstract

Abstract. Structure from motion (SfM) has been widely used to achieve automatic 3D reconstructions. However, as the 3D point clouds obtained via SfM are sparse, multi-view stereo (MVS) was developed to compensate for this sparseness. The accuracy of the 3D surface depends on the accuracy of the orientation elements based on the SfM. Additionally, in the case of an unmanned aerial vehicle (UAV), SfM exhibits a decrease in the accuracy of the orientation elements during complex camera movements. This paper proposes a patch-based MVS (PMVS) method considering the accuracy of the orientation elements. The proposed method involves applying the global SfM, estimating accuracy of exterior orientation (EO) elements, and introducing the accuracy of EO elements to PMVS. The PMVS approximates an object surface by using small rectangular patches, namely local tangent plane approximation. The patches are optimized by minimizing the sum of the photometric discrepancy scores. The accuracy of the EO elements is introduced to the patch optimization as weighting function. This accuracy is defined using the variances of the estimated parameters in the bundle adjustment. We also investigate the types of weighting functions. The results indicate that the proposed method is capable of considering geometric conditions during patch estimation. The proposed method was applied to the three types of image datasets, i.e., images captured using an SLR camera at ground level, images captured using a UAV equipped with a SLR camera, and images captured using an airplane equipped with an oblique camera. Through the experimental results, the improved accuracy and the effectiveness of the proposed method were confirmed.

Highlights

  • A majority of multi-view stereo (MVS) methods generally consist of 4 steps (Scharstein and Szeliski, 2002): (1) matching cost calculations, (2) cost aggregation in the peripheral domain, (3) calculation and optimization of disparity, and (4) improvement of disparity

  • This paper proposes a patch-based MVS (PMVS) method considering the accuracy of the orientation elements

  • A multi-view stereo considering the accuracy of exterior orientation elements was developed

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Summary

INTRODUCTION

A majority of MVS methods generally consist of 4 steps (Scharstein and Szeliski, 2002): (1) matching cost (similarity measure) calculations, (2) cost aggregation in the peripheral domain, (3) calculation and optimization of disparity, and (4) improvement of disparity (filtering). Based on the process of calculating and optimizing disparity, the MVS methods are categorized as local, global, or semi-global methods (Szeliski, 2011). Local methods are advantageous in terms of their computation load They estimate the disparities of pixels by minimizing matching costs, such as intensity difference, in the peripheral domain, i.e., winner-take-all optimization. The accuracy of the 3D surface reconstruction obtained via PMVS depends on the accuracy of the orientation elements based on the SfM. The accuracy of the exterior orientation (EO) elements obtained by SfM is introduced to the patch extension in PMVS.

ESTIMATING EXTERIOR ORIENTATION ACCURACY THROUGH BUNDLE ADJUSTMENT
Image Matching
Global SfM
Estimating EO Accuracy
Basic Models
PMVS Algorithm
Feature matching
Patch expansion
Patch filtering
Incorporating EO Element Accuracies and PMVS
R and translation
Experiment 1
CONCLUSIONS
Full Text
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