Recent Advancements in Learning Algorithms for Point Clouds: An Updated Overview
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Recent advancements in self-driving cars, robotics, and remote sensing have widened the range of applications for 3D Point Cloud (PC) data. This data format poses several new issues concerning noise levels, sparsity, and required storage space; as a result, many recent works address PC problems using Deep Learning (DL) solutions thanks to their capability to automatically extract features and achieve high performances. Such evolution has also changed the structure of processing chains and posed new problems to both academic and industrial researchers. The aim of this paper is to provide a comprehensive overview of the latest state-of-the-art DL approaches for the most crucial PC processing operations, i.e., semantic scene understanding, compression, and completion. With respect to the existing reviews, the work proposes a new taxonomical classification of the approaches, taking into account the characteristics of the acquisition set up, the peculiarities of the acquired PC data, the presence of side information (depending on the adopted dataset), the data formatting, and the characteristics of the DL architectures. This organization allows one to better comprehend some final performance comparisons on common test sets and cast a light on the future research trends.
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An alternative to computing a semantic map in the image space is to produce a semantically labeled pointcloud of the surrounding environment.
“An alternative to the computation of a semantic map in the image space is to produce a semantically labeled pointcloud of the surrounding environment [51].”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [51]
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Region Adaptive Graph Fourier Transform for 3D Point Clouds
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Point Cloud Geometry Scalable Coding With a Single End-to-End Deep Learning Model
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Fast point cloud compression via reversible cellular automata block transform
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Self-citationRoynard, X., Deschaud, J., & Goulette, F. (2018) · The International Journal of Robotics Research · doi:10.1177/0278364918767506 · Cited by 21 · see citing works
A Transform Coding Strategy for Dynamic Point Clouds
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Self-citationHuman Motion Detection Using Planar Array FMCW Radar Through 3D Point Clouds
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On Predictive RAHT For Dynamic Point Cloud Coding
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Correction and interpolation of depth maps from structured light infrared sensors
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Self-citationPoster Abstract: Human Detection with Weak Ranging Signal for FMCW Radar Systems
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A Syndrome-Based Autoencoder For Point Cloud Geometry Compression
Milani, S. (2020) · Proceedings - International Conference on Image Processing · doi:10.1109/icip40778.2020.9190647 · Cited by 11 · see citing works
Self-citationADAE: Adversarial Distributed Source Autoencoder For Point Cloud Compression
Milani, S. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506750 · Cited by 11 · see citing works
Self-citationThe Impact of Environmental Factors on mm-Wave Radar Point-Clouds for Human Activity Recognition
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A Distance Correlation Approach for Optimum Multiscale Selection in 3D Point Cloud Classification
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Cylindrical Coordinates for Lidar Point Cloud Compression
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Rendering-Aware Point Cloud Coding for Mixed Reality Devices
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Self-citationPATCH-AWARE AVERAGING FILTER FOR SCALING IN POINT CLOUD COMPRESSION
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Progressive graph-signal sampling and encoding for static 3D geometry representation
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Improving 3D reconstruction tracks using denoised euclidean distance matrices
Milani, S. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296379 · Cited by 1 · see citing works
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Cited by
Sorted by citations
Rizzoli, G., Barbato, F., & Zanuttigh, P. (2022) · Technologies · doi:10.3390/technologies10040090 · Cited by 58 · see citing works
- Authors
- Camuffo, E., Mari, D., & Milani, S.
- Affiliations
- University of Padua (IT)
- Corresponding author
- Milani, Simone
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2022-02-10
- Volume
- 22
- Issue
- 4
- Pages
- 1357
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC8963024
- OpenAlex
- W4211010347
- Cited by
- 73
- References
- 116
- Citations by year
- 2026: 8 · 2025: 20 · 2024: 22 · 2023: 16 · 2022: 7
- FWCI
- 1.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Padua Research Archive (University of Padova) (submittedVersion, cc-by)
- Funders
- University of Padua (SID 2008 SartreMR project)
- Topics
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Keywords
- 3D point clouds, point cloud processing, semantic scene understanding, point cloud compression, point cloud completion, side information, autonomous driving, robotics, remote sensing
- MeSH terms
- Cloud Computing, Algorithms, Semantics
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