Dryaba A.Yu. Building a 3D-Model of an Object from a Set of Its Images Using a Neural Network Based on the NeRF Algorithm
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https://doi.org/10.15688/mpcm.jvolsu.2023.4.3
Alexander Yu. Dryaba
Postgraduate Student, Department of Computer Sciences and Experimental Mathematics,
Volgograd State University
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,
https://orcid.org/0009-0002-9587-9179
Prosp. Universitetsky, 100, 400062 Volgograd, Russian Federation
Abstract. The work was conducted as part of the development of a computer vision system for analyzing the environment, which could be utilized, for instance, by an autonomous mobile robot. This system involves using a camera to gather information about the surrounding environment. The paper presents methods for reconstructing three-dimensional models of objects solely from a set of 2D-images, using the NeRF algorithm to obtain a representation of a three-dimensional scene in the form of weights of a multilayer perceptron. Each method includes an estimate of the algorithm’s time consumption. Based on the data obtained, it was concluded that it is feasible to recognize the shapes of objects from a natural environment within 5–10 minutes, provided that the neural network training step is transferred to the server side.
Key words: 3D-reconstruction, NeRF, depth map, MLP, volume rendering.
On the Existence and Uniqueness of a Positive Solution to a Boundary Value Problem for a Nonlinear Ordinary Differential Fourth Order Equation by Dryaba A.Yu. is licensed under a Creative Commons Attribution 4.0 International License.
Citation in English: Mathematical Physics and Computer Simulation. Vol. 26 No. 4 2023, pp. 31-42
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