Deep Learning-Based 3D Instance and Semantic Segmentation: A Review
- URL: http://arxiv.org/abs/2406.13308v1
- Date: Wed, 19 Jun 2024 07:56:14 GMT
- Title: Deep Learning-Based 3D Instance and Semantic Segmentation: A Review
- Authors: Siddiqui Muhammad Yasir, Hyunsik Ahn,
- Abstract summary: 3D segmentation is challenging with point cloud data due to substantial redundancy, fluctuating sample density and lack of organization.
Deep learning has been successfully used to a spectrum of 2D vision domains as a prevailing A.I. methods.
This study examines many strategies that have been presented to 3D instance and semantic segmentation.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The process of segmenting point cloud data into several homogeneous areas with points in the same region having the same attributes is known as 3D segmentation. Segmentation is challenging with point cloud data due to substantial redundancy, fluctuating sample density and lack of apparent organization. The research area has a wide range of robotics applications, including intelligent vehicles, autonomous mapping and navigation. A number of researchers have introduced various methodologies and algorithms. Deep learning has been successfully used to a spectrum of 2D vision domains as a prevailing A.I. methods. However, due to the specific problems of processing point clouds with deep neural networks, deep learning on point clouds is still in its initial stages. This study examines many strategies that have been presented to 3D instance and semantic segmentation and gives a complete assessment of current developments in deep learning-based 3D segmentation. In these approaches benefits, draw backs, and design mechanisms are studied and addressed. This study evaluates the impact of various segmentation algorithms on competitiveness on various publicly accessible datasets, as well as the most often used pipelines, their advantages and limits, insightful findings and intriguing future research directions.
Related papers
- A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation [0.20649496811699863]
This paper analyzes recent progress in deep learning methods employed for point cloud processing.
It serves as a comprehensive review on two major tasks in 3D point cloud processing-- namely, 3D shape classification and semantic segmentation.
arXiv Detail & Related papers (2024-05-20T09:33:27Z) - Clustering based Point Cloud Representation Learning for 3D Analysis [80.88995099442374]
We propose a clustering based supervised learning scheme for point cloud analysis.
Unlike current de-facto, scene-wise training paradigm, our algorithm conducts within-class clustering on the point embedding space.
Our algorithm shows notable improvements on famous point cloud segmentation datasets.
arXiv Detail & Related papers (2023-07-27T03:42:12Z) - A Survey of Label-Efficient Deep Learning for 3D Point Clouds [109.07889215814589]
This paper presents the first comprehensive survey of label-efficient learning of point clouds.
We propose a taxonomy that organizes label-efficient learning methods based on the data prerequisites provided by different types of labels.
For each approach, we outline the problem setup and provide an extensive literature review that showcases relevant progress and challenges.
arXiv Detail & Related papers (2023-05-31T12:54:51Z) - A Threefold Review on Deep Semantic Segmentation: Efficiency-oriented,
Temporal and Depth-aware design [77.34726150561087]
We conduct a survey on the most relevant and recent advances in Deep Semantic in the context of vision for autonomous vehicles.
Our main objective is to provide a comprehensive discussion on the main methods, advantages, limitations, results and challenges faced from each perspective.
arXiv Detail & Related papers (2023-03-08T01:29:55Z) - PointResNet: Residual Network for 3D Point Cloud Segmentation and
Classification [18.466814193413487]
Point cloud segmentation and classification are some of the primary tasks in 3D computer vision.
In this paper, we propose PointResNet, a residual block-based approach.
Our model directly processes the 3D points, using a deep neural network for the segmentation and classification tasks.
arXiv Detail & Related papers (2022-11-20T17:39:48Z) - Point Discriminative Learning for Unsupervised Representation Learning
on 3D Point Clouds [54.31515001741987]
We propose a point discriminative learning method for unsupervised representation learning on 3D point clouds.
We achieve this by imposing a novel point discrimination loss on the middle level and global level point features.
Our method learns powerful representations and achieves new state-of-the-art performance.
arXiv Detail & Related papers (2021-08-04T15:11:48Z) - Deep Learning Based 3D Segmentation: A Survey [42.44509605101214]
3D segmentation is a fundamental and challenging problem in computer vision with applications in autonomous driving and robotics.
Deep learning techniques have recently become the tool of choice for 3D segmentation tasks.
This paper comprehensively surveys the recent progress in deep learning-based 3D segmentation techniques.
arXiv Detail & Related papers (2021-03-09T13:58:35Z) - Improving Point Cloud Semantic Segmentation by Learning 3D Object
Detection [102.62963605429508]
Point cloud semantic segmentation plays an essential role in autonomous driving.
Current 3D semantic segmentation networks focus on convolutional architectures that perform great for well represented classes.
We propose a novel Aware 3D Semantic Detection (DASS) framework that explicitly leverages localization features from an auxiliary 3D object detection task.
arXiv Detail & Related papers (2020-09-22T14:17:40Z) - Deep Learning for 3D Point Cloud Understanding: A Survey [16.35767262996978]
The development of practical applications, such as autonomous driving and robotics, has brought increasing attention to 3D point cloud understanding.
Deep learning has achieved remarkable success on image-based tasks, but there are many unique challenges faced by deep neural networks in processing massive, unstructured and noisy 3D points.
This paper summarizes recent remarkable research contributions in this area from several different directions.
arXiv Detail & Related papers (2020-09-18T16:34:12Z) - Deep Learning for 3D Point Clouds: A Survey [58.954684611055]
This paper presents a review of recent progress in deep learning methods for point clouds.
It covers three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation.
It also presents comparative results on several publicly available datasets.
arXiv Detail & Related papers (2019-12-27T09:15:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.