title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¼ÐµÑÐ¾Ð´Ð° Ð°ÑÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð´Ð°Ð½Ð½ÑÑ Ð² Ð¾Ð±Ð»Ð°ÐºÐ°Ñ ÑÐ¾ÑÐµÐº
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Development of the data augmentation method in point clouds
Creators: name: ÐÑÐ´Ð¾Ð², ÐÐ¸ÐºÐ¸ÑÐ°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: Ð¾Ð±Ð»Ð°ÐºÐ° ÑÐ¾ÑÐµÐº
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ
Ð°ÑÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ñ
Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
point clouds
machine learning
neural networks
classification
augmentation
generative neural networks

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð¿Ð¾ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÑÑÑÑÐ¼ÐµÑÐ½ÑÑ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² Ð¸ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð° Ð¿Ð¾ ÐºÐ°ÑÐµÑÑÐ²ÐµÐ½Ð½Ð¾Ð¼Ñ ÑÐ»ÑÑÑÐµÐ½Ð¸Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ¾Ð². ÐÑÐ» ÑÐ¾Ð±ÑÐ°Ð½ Ð¸ Ð¿ÑÐ¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½ Ð¼Ð°ÑÐµÑÐ¸Ð°Ð» Ð½Ð° ÑÐµÐ¼Ñ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð¾Ð±Ð»Ð°ÐºÐ¾Ð² ÑÐ¾ÑÐµÐº, Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ñ Ð¿Ð¾Ð´ÑÐ¾Ð´Ñ Ðº ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÑÑÑÑÐ¼ÐµÑÐ½ÑÑ Ð¾Ð±Ð»Ð°ÐºÐ¾Ð². Ð ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð° ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð¾Ð±Ð»Ð°ÐºÐ¾Ð² ÑÐ¾ÑÐµÐº Ð½Ð° Ð±Ð°Ð·Ðµ ÑÐ²ÑÑÑÐ¾ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð¸ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÐµÑÐ½Ð¸Ðº Ð°ÑÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸. ÐÐ¾Ð»ÑÑÐµÐ½Ð½ÑÐµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¿ÑÐ¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ñ Ð¸ ÑÐ´ÐµÐ»Ð°Ð½Ñ Ð²ÑÐ²Ð¾Ð´Ñ Ð¾ Ð²Ð»Ð¸ÑÐ½Ð¸Ð¸ Ð°ÑÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð½Ð° Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²ÑÑ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ¾Ð². Ð¡ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾Ð¹ ÑÐµÑÐ¸ ÑÐ¸Ð½ÑÐµÐ·Ð¸ÑÐ¾Ð²Ð°Ð½Ñ Ð½Ð¾Ð²ÑÐµ ÑÐ¾ÑÐ¼Ñ Ð¾Ð±Ð»Ð°ÐºÐ¾Ð² ÑÐ¾ÑÐµÐº. ÐÐ¾ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ°Ð¼ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½ Ð¿Ð¾Ð´ÑÐ¾Ð´, Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½ÑÐ¹ Ð½Ð° ÑÐ¾ÑÐµÑÐ°Ð½Ð¸Ð¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ñ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¸ Ð°ÑÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹, Ð´Ð»Ñ Ð²Ð½ÐµÐ´ÑÐµÐ½Ð¸Ñ Ð² Ð¿ÑÐ¾Ð¼ÑÑÐ»ÐµÐ½Ð½ÑÐµ ÑÐ¸ÑÑÐµÐ¼Ñ. ÐÐ°Ð½Ð½ÑÐ¹ Ð¿Ð¾Ð´ÑÐ¾Ð´ Ð´Ð¾Ð»Ð¶ÐµÐ½ ÑÐ½Ð¸Ð·Ð¸ÑÑ Ð²ÐµÑÐ¾ÑÑÐ½Ð¾ÑÑÑ Ð¾ÑÐ¸Ð±ÐºÐ¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ¾Ð².
description: This work is devoted to the study of neural networks for the classification of three-dimensional objects and the development of an approach for the qualitative improvement of classifier training. The material on the processing of point clouds was collected and analyzed, approaches to the classification of three-dimensional clouds were investigated. The paper classifies point clouds based on convolutional neural networks and using augmentation techniques. The results obtained are analyzed and conclusions are drawn about the influence of augmentation on the training of neural network classifiers. With the help of a generative network, new forms of point clouds have been synthesized. Based on the results of the study, an approach based on a combination of the use of generative models and augmentation networks for implementation in industrial systems is proposed. This approach should reduce the probability of classifiers error.