RESEARCH ON THE ACCURACY ENHANCEMENT OF SFM POINT CLOUD DATA FOR CONCRETE STRUCTURES THROUGH PATTERN PROJECTION

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  • コンクリート構造物を対象とした模様の投影によるSfM点群データの高精度化に関する研究

Abstract

<p> With the rise of i-Construction in Japan, there has been an increase in opportunities to acquire point cloud data that captures the surface layers of the earth in three dimensions are increasing. Structure from Motion (SfM), which can produce point cloud data from multi-view images, is particularly noteworthy. However, because SfM determines corresponding points and self-positions based on the similarity of feature values derived from the RGB values of each pixel, it struggles to generate dense point cloud data for objects that have minimal color variation, such as a white wall. In previous research, we proposed a technique to enhance the density and accuracy of SfM point cloud data by projecting a pattern onto the object with a projector. However, this method introduced the issue of creating artificial unevenness that doesn't actually exist on the object. In this study, we devised an optimized pattern to generate accurate point cloud data and validated its efficacy with demonstration experiments on a bridge pier model. The results showed that, using our method, SfM can produce point cloud data with an error margin of less than 10 mm when compared to laser-based equipment.</p>

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