Experimental evaluation of the recommendations of the method for accompanying the operator while taking photos for subsequent 3D reconstruction

The availability of 3D reconstruction technologies expands the possibilities of their application for analyzing the state of complex technical objects. However, solutions for 3D reconstruction still heavily depend on the quality of the input data and require significant time investments to build high-quality 3D models. The goal of this study is to expedite the process of constructing detailed and defect-free 3D models by creating an automated method to assist the operator during the capture of RGB images for subsequent 3D reconstruction. The method is used for preliminary analysis of the collected data and allows for the identification of problematic frames in advance, as well as determining the areas of the object to be captured that will be reconstructed with defects or low level of detail. The study describes algorithms for the two final stages of the method - global analysis and recommendations, based on the real-time construction of sparse point clouds and 3D models, as well as their properties assessment in conjunction with the operator's trajectory, reconstructed from accelerometer and gyroscope data. The effectiveness of the final stages of the method, based on the proposed algorithms, was evaluated through experiments. It was demonstrated that the use of global analysis and recommendation stages not only allows for predicting defective areas and low-detail zones in dense 3D models on mobile devices, but also accelerates the process of obtaining a high-quality 3D model by at least five times through automated analysis of the collected data.

Authors: R. P. Shestopalov, D. V. Ivanov, M. M. Zaslavskiy, A. P. Grebenshchikov

Direction: Informatics, Computer Technologies And Control

Keywords: three-dimensional reconstruction, photogrammetry, structure from motion, data collection, mobile applications for RGB data analysis

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