Version 1
: Received: 11 November 2021 / Approved: 12 November 2021 / Online: 12 November 2021 (14:56:14 CET)
How to cite:
Burume, D.M.; Du, S. Deep Learning Methods Applied to 3D Point Clouds Based Instance Segmentation: A Review. Preprints2021, 2021110228. https://doi.org/10.20944/preprints202111.0228.v1
Burume, D.M.; Du, S. Deep Learning Methods Applied to 3D Point Clouds Based Instance Segmentation: A Review. Preprints 2021, 2021110228. https://doi.org/10.20944/preprints202111.0228.v1
Burume, D.M.; Du, S. Deep Learning Methods Applied to 3D Point Clouds Based Instance Segmentation: A Review. Preprints2021, 2021110228. https://doi.org/10.20944/preprints202111.0228.v1
APA Style
Burume, D.M., & Du, S. (2021). Deep Learning Methods Applied to 3D Point Clouds Based Instance Segmentation: A Review. Preprints. https://doi.org/10.20944/preprints202111.0228.v1
Chicago/Turabian Style
Burume, D.M. and Shengzhi Du. 2021 "Deep Learning Methods Applied to 3D Point Clouds Based Instance Segmentation: A Review" Preprints. https://doi.org/10.20944/preprints202111.0228.v1
Abstract
Beyond semantic segmentation,3D instance segmentation(a process to delineate objects of interest and also classifying the objects into a set of categories) is gaining more and more interest among researchers since numerous computer vision applications need accurate segmentation processes(autonomous driving, indoor navigation, and even virtual or augmented reality systems…) This paper gives an overview and a technical comparison of the existing deep learning architectures in handling unstructured Euclidean data for the rapidly developing 3D instance segmentation. First, the authors divide the 3D point clouds based instance segmentation techniques into two major categories which are proposal based methods and proposal free methods. Then, they also introduce and compare the most used datasets with regard to 3D instance segmentation. Furthermore, they compare and analyze these techniques performance (speed, accuracy, response to noise…). Finally, this paper provides a review of the possible future directions of deep learning for 3D sensor-based information and provides insight into the most promising areas for prospective research.
Keywords
Deep Learning; 3D Instance Segmentation; Datasets
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.