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Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey

Shahab Saquib Sohail, Yassine Himeur, Hamza Kheddar, Abbes Amira, Fodil Fadli, Shadi Atalla, Abigail Copiaco, Wathiq Mansoor

TL;DR

The paper addresses the challenge of limited labeled data and domain shifts in 3D point cloud understanding by surveying deep transfer learning (DTL) and domain adaptation (DA) techniques. It synthesizes foundational concepts, taxonomy, datasets, metrics, and a broad spectrum of applications including segmentation, classification, registration, and scene generation, while highlighting open challenges and future directions. Key contributions include the first comprehensive review of DTL/DA for 3DPC, a well-defined taxonomy of transfer scenarios, an inventory of datasets and evaluation metrics, and practical guidance on open problems and promising research directions such as 3DPC transformers, cross-modal learning, and diffusion-based generation. The findings underscore the potential of DTL/DA to reduce labeling costs, improve cross-domain generalization, and enable scalable 3DPC understanding across robotics, autonomous systems, and related fields.

Abstract

The 3D point cloud (3DPC) has significantly evolved and benefited from the advance of deep learning (DL). However, the latter faces various issues, including the lack of data or annotated data, the existence of a significant gap between training data and test data, and the requirement for high computational resources. To that end, deep transfer learning (DTL), which decreases dependency and costs by utilizing knowledge gained from a source data/task in training a target data/task, has been widely investigated. Numerous DTL frameworks have been suggested for aligning point clouds obtained from several scans of the same scene. Additionally, DA, which is a subset of DTL, has been modified to enhance the point cloud data's quality by dealing with noise and missing points. Ultimately, fine-tuning and DA approaches have demonstrated their effectiveness in addressing the distinct difficulties inherent in point cloud data. This paper presents the first review shedding light on this aspect. it provides a comprehensive overview of the latest techniques for understanding 3DPC using DTL and domain adaptation (DA). Accordingly, DTL's background is first presented along with the datasets and evaluation metrics. A well-defined taxonomy is introduced, and detailed comparisons are presented, considering different aspects such as different knowledge transfer strategies, and performance. The paper covers various applications, such as 3DPC object detection, semantic labeling, segmentation, classification, registration, downsampling/upsampling, and denoising. Furthermore, the article discusses the advantages and limitations of the presented frameworks, identifies open challenges, and suggests potential research directions.

Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey

TL;DR

The paper addresses the challenge of limited labeled data and domain shifts in 3D point cloud understanding by surveying deep transfer learning (DTL) and domain adaptation (DA) techniques. It synthesizes foundational concepts, taxonomy, datasets, metrics, and a broad spectrum of applications including segmentation, classification, registration, and scene generation, while highlighting open challenges and future directions. Key contributions include the first comprehensive review of DTL/DA for 3DPC, a well-defined taxonomy of transfer scenarios, an inventory of datasets and evaluation metrics, and practical guidance on open problems and promising research directions such as 3DPC transformers, cross-modal learning, and diffusion-based generation. The findings underscore the potential of DTL/DA to reduce labeling costs, improve cross-domain generalization, and enable scalable 3DPC understanding across robotics, autonomous systems, and related fields.

Abstract

The 3D point cloud (3DPC) has significantly evolved and benefited from the advance of deep learning (DL). However, the latter faces various issues, including the lack of data or annotated data, the existence of a significant gap between training data and test data, and the requirement for high computational resources. To that end, deep transfer learning (DTL), which decreases dependency and costs by utilizing knowledge gained from a source data/task in training a target data/task, has been widely investigated. Numerous DTL frameworks have been suggested for aligning point clouds obtained from several scans of the same scene. Additionally, DA, which is a subset of DTL, has been modified to enhance the point cloud data's quality by dealing with noise and missing points. Ultimately, fine-tuning and DA approaches have demonstrated their effectiveness in addressing the distinct difficulties inherent in point cloud data. This paper presents the first review shedding light on this aspect. it provides a comprehensive overview of the latest techniques for understanding 3DPC using DTL and domain adaptation (DA). Accordingly, DTL's background is first presented along with the datasets and evaluation metrics. A well-defined taxonomy is introduced, and detailed comparisons are presented, considering different aspects such as different knowledge transfer strategies, and performance. The paper covers various applications, such as 3DPC object detection, semantic labeling, segmentation, classification, registration, downsampling/upsampling, and denoising. Furthermore, the article discusses the advantages and limitations of the presented frameworks, identifies open challenges, and suggests potential research directions.
Paper Structure (68 sections, 10 equations, 15 figures, 4 tables)

This paper contains 68 sections, 10 equations, 15 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Preliminary
  3. Our contributions
  4. Methodology of the survey
  5. Background
  6. 3D Point Cloud
  7. Operations and Transformations
  8. Definitions pertaining to DTL
  9. Theoretical taxonomy of DTL
  10. Inductive DTL
  11. Transductive DTL
  12. Adversarial DTL
  13. Popular DL-based PC models
  14. Feature Extraction and Geometric Detail Enhancement Models
  15. Segmentation and Object Detection Models
  16. ...and 53 more sections

Figures (15)

  • Figure 1: Difference between conventional ML and DTL techniques for multiple tasks: (a) conventional ML and (b) DTL.
  • Figure 2: Tasks and challenges related to data and DL/DTL-based applications on 3DPC.
  • Figure 3: Summary of the approach used to search and select articles included in the review: (a) The adopted search procedure and (b) The selection criteria.
  • Figure 4: Survey structure with sections and sub-sections disctribution.
  • Figure 5: Proposed taxonomy of existing DTL algorithms for 3DPC.
  • ...and 10 more figures