Derin Öğrenme Yöntemleri ile 3B Nokta Bulutlarının Semantik Segmentasyonuna Genel bir Bakış

Year 2023, Volume: 11 Issue: 1, 342 - 357, 31.01.2023

Muhammed Ahmet Demirtaş

https://doi.org/10.29130/dubited.1004211

Abstract

Semantik segmentasyon, çevredeki nesnelere anlam vermek için etiketlenmiş her pikseli anlamlı bir sınıfa atayan bir veri işleme yöntemidir. Derin öğrenme (DÖ) tabanlı yöntemlerin geliştirilmesi, Nokta Bulutu (NB) ile segmentasyon yöntemlerine olan ilgiyi artırmıştır. 3 Boyutlu (3B) nokta bulutu semantik segmentasyonu, farklı tarama araçları ile elde edilen 3B veri setlerinde aynı bölgede aynı özelliklere sahip noktaları homojen bölgelere ayırmaktadır. 3B nokta bulutları ile 3B nesneleri anlamak için semantik segmentasyonun kullanılması önemli bir başlangıç olmuştur. Özellikle derin öğrenme yöntemlerinin kullanılması bu alanı odak noktası haline getirmiştir. 3B yapılandırılmamış büyük nokta bulutlarını işlerken, derin öğrenmeyi temel alarak geliştirilen yeni yöntemler, yaklaşımlar ve modeller üzerinde benzersiz sorunlarla karşılaşılması bu alanın gelişime açık olduğunu göstermektedir. Bu yeni yöntemlerin başarılarını anlamak için, kıyaslama veri kümeleri: ShapeNet, S3dis, ScanNet, SemanticKITTI üzerindeki performansları değerlendirilmiş. 3B nokta bulutu ile segmentasyon alanına katkıda bulunan dikkate değer araştırmalar incelenmiş, avantajları, dezavantajları ve önerilen yöntemlerin katkıları sunulmuştur. Sunulan tüm yöntemlerin mimari yapısı, yaygın olarak kullanılan veri kümeleri üzerindeki başarıları tartışılmış ve gelecekteki araştırmalara öncülük edecek bilgiler önerilmiştir.

Keywords

3B Nokta Bulutu, Derin Öğrenme, Semantik Segmentasyon, 3D Point Cloud, Deep Learning, Semantic Segmentation

Review on Semantic Segmentation of 3D Point Clouds with Deep Learning Methods

Abstract

Semantic segmentation is a data processing method that assigns each labeled pixel to a meaningful class to give meaning to surrounding objects. The development of deep learning-based methods has increased the interest in point cloud segmentation processes. 3D point cloud semantic segmentation in 3D datasets obtained with different scanning tools divides, points with the same feature in the same region are divided into homogeneous regions. The use of semantic segmentation to understand 3D point clouds with 3D objects has been an important start. In particular, the use of deep learning methods has made this area a focal point. In particular, When processing large unstructured 3D point clouds, encountering unique problems on new methods and approaches developed on the based on deep learning has shown that this area needs to be improved. In order to understand the achievements of these new methods, their performance on 3D benchmark datasets ShapeNet, S3dis, ScanNet, SemanticKITTI was evaluated. Important research contributing to the segmentation field with 3D point cloud is analyzed, the advantages, disadvantages and contributions of the proposed methods are presented. The all the presented methods and their achievements on widely used datasets are discussed and he offered information that would lead to future research. 

Keywords

3D Point Cloud, Deep Learning, Semantic Segmentation

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