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Recent Advancements in Learning Algorithms for Point Clouds: An Updated Overview.

Elena Camuffo¹, Daniele Mari¹, Simone Milani¹

¹Department of Information Engineering, University of Padova, Via Gradenigo 6/A, 35131 Padova, Italy.

Sensors (Basel, Switzerland)

|February 26, 2022

Summary

This summary is machine-generated.

Deep Learning (DL) offers advanced solutions for 3D Point Cloud (PC) challenges like noise and sparsity. This review categorizes DL approaches for PC semantic scene understanding, compression, and completion, guiding future research.

Keywords:

completion compression deep learning point cloud scene understanding semantic segmentation

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Area of Science:

Computer Vision
Artificial Intelligence
Data Science

Background:

3D Point Cloud (PC) data is increasingly vital for self-driving cars, robotics, and remote sensing.
Challenges with PC data include noise, sparsity, and large storage requirements.
Deep Learning (DL) has emerged as a powerful tool for addressing these PC data issues.

Purpose of the Study:

To provide a comprehensive overview of state-of-the-art Deep Learning (DL) approaches for 3D Point Cloud (PC) processing.
To introduce a novel taxonomical classification for DL methods applied to PC data.
To analyze DL techniques for semantic scene understanding, compression, and completion of PC data.

Main Methods:

A new classification framework for DL approaches based on acquisition setup, PC data characteristics, side information, data formatting, and DL architecture.
Review and synthesis of recent literature on DL for PC processing.
Performance comparison of different DL approaches on standard datasets.

Main Results:

The proposed taxonomy offers a structured way to understand diverse DL techniques for PC data.
Identifies key DL architectures and strategies for semantic scene understanding, compression, and completion.
Highlights performance variations based on data characteristics and acquisition setups.

Conclusions:

DL is crucial for overcoming 3D Point Cloud (PC) data limitations.
The new classification aids in navigating the complex landscape of DL for PC processing.
Future research directions are identified, focusing on improved DL models and data handling techniques for PC data.