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