Deep Learning on 3D Semantic Segmentation: A Detailed Review

TL;DR

This paper provides a comprehensive review of deep learning methods for 3D semantic segmentation, proposing a new taxonomy scheme to standardize classification, and offering extensive datasets and analysis to guide future research.

Contribution

It introduces a new taxonomy scheme for classifying 3DSS deep learning methods, enhancing clarity and comparability across studies.

Findings

Proposed a standardized taxonomy scheme for 3DSS methods.

Compiled and analyzed over 400 3DSS methods and datasets.

Provided insights into evaluation metrics and loss functions used in 3DSS.

Abstract

In this paper an exhaustive review and comprehensive analysis of recent and former deep learning methods in 3D Semantic Segmentation (3DSS) is presented. In the related literature, the taxonomy scheme used for the classification of the 3DSS deep learning methods is ambiguous. Based on the taxonomy schemes of 9 existing review papers, a new taxonomy scheme of the 3DSS deep learning methods is proposed, aiming to standardize it and improve the comparability and clarity across related studies. Furthermore, an extensive overview of the available 3DSS indoor and outdoor datasets is provided along with their links. The core part of the review is the detailed presentation of recent and former 3DSS deep learning methods and their classification using the proposed taxonomy scheme along with their GitHub repositories. Additionally, a brief but informative analysis of the evaluation metrics and loss functions used in 3DSS is included. Finally, a fruitful discussion of the examined 3DSS methods and datasets, is presented to foster new research directions and applications in the field of 3DSS. Supplementary, to this review a GitHub repository is provided (https://github.com/thobet/Deep-Learning-on-3D-Semantic-Segmentation-a- Detailed-Review) including a quick classification of over 400 3DSS methods, using the proposed taxonomy scheme.