Derin öğrenme ile 3B nokta bulutlarının sınıflandırılmasına genel bir bakış

Year 2022, , 1 - 9, 30.03.2022

Muhammed Ahmet Demirtaş

https://doi.org/10.24012/dumf.1067736

Cited By: 1

Abstract

Nokta bulutu (NB) bir vektör uzayında nesneye ait bilgilerin x,y,z koordinat sisteminde matematiksel olarak temsil edilen noktalar kümesidir. Noktalar kaydedilen uzaysal koordinat sisteminde sınıflandırılarak nesne ya da bir alanı ifade eden anlamsal bilgileri tanımlarlar. 3 boyutlu (3B) nokta bulutu gelişen teknolojilerle beraber nesneleri sınıflandırma, algılama ve tanıma alanlarında son zamanlarda oldukça popüler bir hale gelmiştir. Lazer tarama sistemleri ile taranan nesneler 3B nokta bulutuna dönüştürülmüş ve bu verilerin sanal ortama aktarılması ile farklı veri setleri elde edilmiştir. 3B noktasal verileri derin ağlar ile başarılı bir şekilde sınıflandırmak için geliştirilen yöntemler detaylı olarak incelenmiştir. Nokta bulutlarına 3B koordinat sistemi ile birlikte farklı bilgiler dâhil ederek (derinlik ya da RGB(red-green-blue)) farklı boyutlarda veya farklı yoğunlukta nokta bulutları oluşturulmuştur. Ayrıca nokta bulutu veri kümesindeki her bir noktaya ait; harici veya dâhili bilgiler eklenmiş RGB değerleri ile nesneler renklendirilmiştir. Bu araştırmada 3B nokta bulutunu derin ağlar ile sınıflandıran yöntemlerin başarı performansları, avantajları, dezavantajları analiz edilmiştir. Özellikle uygulanan algoritmalar, denenen yöntemler ve oluşturulan modeller karşılaştırılmış ve tartışılmıştır. Son olarak makalede gelecekteki çalışmalara hız ve yön vermesi için güncel yöntemler kapsamlı bir şekilde sunulmuştur.

Keywords

Nokta bulutu, dering öğrenme, 3B nesne sınıflandırma

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