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Intelligent Processing of 3D Point Clouds for Scene Understanding and Modelling

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2405

Special Issue Editors

Dr. Yuan Li

E-Mail Website
Guest Editor
School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai 519082, China
Interests: geoinformation; 3D modelling; photogrammetry; 3D point cloud
Special Issues, Collections and Topics in MDPI journals
Dr. Rong Huang

E-Mail Website
Guest Editor
College of Surveying and Geo-Informatics, Tongji University, Shanghai, China
Interests: planetary remote sensing; 3D point cloud processing
Dr. Shuhang Zhang

E-Mail Website
Guest Editor
School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China
Interests: photogrammetry; 3D mapping and modelling; UAV route planning; point cloud analysis
Dr. Linfu Xie

E-Mail Website
Guest Editor
School of Architecture and Urban Planning, Shenzhen Univiersity, Shenzhen 518060, China
Interests: 3D city modelling; photogrammetry; building reconstruction

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue titled “Intelligent Processing of 3D Point Clouds for Scene Understanding and Modelling” in Remote Sensing, an international, peer-reviewed, open access journal focused on the science and applications of remote sensing technology. For more information about the journal, please visit https://www.mdpi.com/journal/remotesensing.

Point clouds acquired using Light Detection and Ranging (LiDAR) technology or photogrammetric methods have become important 3D data with high density and high precision, facilitating the understanding and modelling of 3D environments. Point clouds provide rich, multi-dimensional spatial information, including attributes such as intensity, colour, and multiple echoes. However, despite their potential, point clouds are often characterised by high complexity, disorder, and massive scale, posing significant challenges in data processing, interpretation, and management.

In this Special Issue, we aim to highlight the latest research in intelligent methods for LiDAR point cloud processing, including the integration of deep learning and artificial intelligence techniques, to address the challenges associated with 3D scene understanding and digital surface modelling. This Special Issue seeks to feature cutting-edge solutions that focus on the effective and efficient extraction of meaningful information from point clouds, such as semantic segmentation, object recognition, and the modelling of urban, natural and planetary environments.

This Special Issue aims to focus on the state-of-the-art methodologies and applications in 3D point cloud processing for scene understanding and modelling. Its scope aligns with the broader goals of remote sensing research, highlighting innovative approaches in deep learning, multi-modal data fusion, 3D visual grounding, and intelligent development that push the boundaries of geographic mapping and 3D model generation.

We invite submissions on a variety of topics, including, but not limited to, the following:

Semantic/Instance segmentation of 3D point clouds;

AI-driven 3D object/geometric primitive detection;

Multi-sensor/Multi-platform data integration;

Point cloud registration;

Intelligent applications of point clouds in urban modelling/forest inventory/environmental monitoring/planetary exploration;

3D visual grounding;

Advanced SLAM (Simultaneous Localization and Mapping) with LiDAR;

Deep learning algorithms for large-scale space-borne LiDAR processing;

Generative 3D modelling based on point clouds.

Dr. Yuan Li
Dr. Rong Huang
Dr. Shuhang Zhang
Dr. Linfu Xie
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • LiDAR
  • point cloud processing
  • deep learning
  • artificial intelligence
  • 3D mapping and modelling

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

26 pages, 47051 KiB  
Article
Dynamic Light Path and Bidirectional Reflectance Effects on Solar Noise in UAV-Borne Photon-Counting LiDAR
by Kuifeng Luan, Jinhui Zheng, Wei Kong, Weidong Zhu, Lizhe Zhang, Peiyao Zhang and Lin Liu
Remote Sens. 2025, 17(10), 1708; https://doi.org/10.3390/rs17101708 - 13 May 2025
Viewed by 227
Abstract
Accurate solar background noise modeling in island-reef LiDAR surveys is hindered by anisotropic coastal reflectivity and dynamic light paths, which isotropic models fail to address. We propose BNR-B, a bidirectional reflectance distribution function (BRDF)-based noise model that integrates solar-receiver geometry with micro-facet scattering [...] Read more.
Accurate solar background noise modeling in island-reef LiDAR surveys is hindered by anisotropic coastal reflectivity and dynamic light paths, which isotropic models fail to address. We propose BNR-B, a bidirectional reflectance distribution function (BRDF)-based noise model that integrates solar-receiver geometry with micro-facet scattering dynamics. Validated via single-photon LiDAR field tests on diverse coastal terrains at Jiajing Island, China, BNR-B reveals the following: (1) Solar zenith/azimuth angles non-uniformly modulate noise fields—higher solar zenith angles reduce noise intensity and homogenize spatial distribution; (2) surface reflectivity linearly correlates with noise rate (R2 > 0.99), while roughness governs scattering directionality through micro-facet redistribution. BNR-B achieves 28.6% higher noise calculation accuracy than Lambertian models, with a relative phase error < 2% against empirical data. As the first BRDF-derived solar noise correction framework for coastal LiDAR, it addresses critical limitations of isotropic assumptions by resolving directional noise modulation. The model’s adaptability to marine–terrestrial interfaces enhances precision in coastal monitoring and submarine mapping, offering transformative potential for geospatial applications requiring photon-counting LiDAR in complex environments. Key innovations include dynamic coupling of geometric optics and surface scattering physics, enabling robust spatiotemporal noise quantification, critical for high-resolution terrain reconstruction. Full article
(This article belongs to the Special Issue Intelligent Processing of 3D Point Clouds for Scene Understanding and Modelling)
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20 pages, 3024 KiB  
Article
Building Lightweight 3D Indoor Models from Point Clouds with Enhanced Scene Understanding
by Minglei Li, Mingfan Li, Min Li and Leheng Xu
Remote Sens. 2025, 17(4), 596; https://doi.org/10.3390/rs17040596 - 10 Feb 2025
Viewed by 918
Abstract
Indoor scenes often contain complex layouts and interactions between objects, making 3D modeling of point clouds inherently difficult. In this paper, we design a divide-and-conquer modeling method considering the structural differences between indoor walls and internal objects. To achieve semantic understanding, we propose [...] Read more.
Indoor scenes often contain complex layouts and interactions between objects, making 3D modeling of point clouds inherently difficult. In this paper, we design a divide-and-conquer modeling method considering the structural differences between indoor walls and internal objects. To achieve semantic understanding, we propose an effective 3D instance segmentation module using a deep network Indoor3DNet combined with super-point clustering, which provides a larger receptive field and maintains the continuity of individual objects. The Indoor3DNet includes an efficient point feature extraction backbone with good operability for different object granularity. In addition, we use a geometric primitives-based modeling approach to generate lightweight polygonal facets for walls and use a cross-modal registration technique to fit the corresponding instance models for internal objects based on their semantic labels. This modeling method can restore correct geometric shapes and topological relationships while maintaining a very lightweight structure. We have tested the method on diverse datasets, and the experimental results demonstrate that the method outperforms the state-of-the-art in terms of performance and robustness. Full article
(This article belongs to the Special Issue Intelligent Processing of 3D Point Clouds for Scene Understanding and Modelling)
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Graphical abstract

19 pages, 7595 KiB  
Article
A Two-Stage Nearshore Seafloor ICESat-2 Photon Data Filtering Method Considering the Spatial Relationship
by Longjiao Zuo, Xuying Wang, Qianzhe Sun, Jian Shi and Yunsheng Zhang
Remote Sens. 2024, 16(24), 4795; https://doi.org/10.3390/rs16244795 - 23 Dec 2024
Viewed by 611
Abstract
“Ice, Cloud, and Land Elevation Satellite-2” (ICESat-2) produces photon-point clouds that can be used to obtain nearshore bathymetric data through density-based filtering methods. However, most traditional methods simplified the variable spatial density distribution of a photon to a linear relationship with water depth, [...] Read more.
“Ice, Cloud, and Land Elevation Satellite-2” (ICESat-2) produces photon-point clouds that can be used to obtain nearshore bathymetric data through density-based filtering methods. However, most traditional methods simplified the variable spatial density distribution of a photon to a linear relationship with water depth, causing a limited extraction effect. To address this limitation, we propose a two-stage filtering method that considers spatial relationships. Stage one constructs the adaptive photon density threshold by mapping a nonlinear relationship between the water depth and photon density to obtain initial signal photons. Stage two adopts a seed-point expanding method to fill gaps in initial signal photons to obtain continuous signal photons that more fully reflect seabed topography. The proposed method is applied to ICESat-2 data from Oahu Island and compared with three other density-based filtering methods: AVEBM (Adaptive Variable Ellipse filtering Bathymetric Method), Bimodal Gaussian fitting, and Quadtree Isolation. Our method (F-measure, F = 0.803) outperforms other methods (F = 0.745, 0.598, and 0.454, respectively). The accuracy of bathymetric data gained from seabed photons filtered using our method can achieve 0.615 m (Mean Absolute Error) and 0.716 m (Root Mean Squared Error). We demonstrate the effectiveness of incorporating photon spatial relationships to enhance the filtering of seabed signal photons. Full article
(This article belongs to the Special Issue Intelligent Processing of 3D Point Clouds for Scene Understanding and Modelling)
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