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Intelligent Point Cloud Processing, Sensing and Understanding—Fourth Edition
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Intelligent Point Cloud Processing, Sensing and Understanding—Fourth Edition

A Special Issue of Sensors (ISSN 1424-8220) belonging to the section "Sensing and Imaging".

Deadline for manuscript submissions: 20 December 2026 | Viewed by 2581

Editor

Dr. Miaohui Wang

E-Mail Website
Guest Editor
School of Information Engineering, Shenzhen University, Shenzhen 518052, China
Interests: computer vision and machine learning; image/video processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of the previous Special Issues, “Intelligent Point Cloud Processing, Sensing and Understanding” (https://www.mdpi.com/journal/sensors/special_issues/IX18KRFUQ1); “Intelligent Point Cloud Processing, Sensing and Understanding (Volume II)” (https://www.mdpi.com/journal/sensors/special_issues/ZW695MGH36) and “Intelligent Point Cloud Processing, Sensing and Understanding—Third Edition” (https://www.mdpi.com/journal/sensors/special_issues/8038K4KE01), we are pleased to announce the next in the series, “Intelligent Point Cloud Processing, Sensing and Understanding—Fourth Edition”.

Point clouds are deemed to be one of the foundational pillars representing the 3D digital world, despite irregular topologies among discrete points. Recently, the advancements in sensor technologies that acquire point cloud data for flexible and scalable geometric representation have paved the way for the development of new ideas, methodologies and solutions in countless remote sensing applications. State-of-the-art sensors are capable of capturing and describing objects in a scene by using dense point clouds from various platforms (satellites, aerial, UAVs, vehicle-borne, backpacks, handheld and static terrestrial); perspectives (nadir, oblique, and side view); spectra (multispectral) and levels of granularity (point density and completeness). Meanwhile, the ever-expanding application areas of point cloud processing have already covered not only conventional domains in geospatial analysis but also manufacturing, civil engineering, construction, transportation, ecology, forestry and mechanical engineering, amongst others.

This Special Issue aims to include contributions that focus on processing and utilizing point cloud data acquired from laser scanners and other 3D imaging systems. We are particularly interested in original papers that address innovative techniques for generating, handling and analyzing point cloud data; challenges in dealing with point cloud data in emerging remote sensing applications and the development of new applications for point cloud data.

Dr. Miaohui Wang
Guest Editor

Manuscript Submission Information

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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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors 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 2600 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

  • point cloud acquisition from laser scanners, stereo vision, panoramas, camera phone images and oblique as well as satellite imagery
  • deep learning for point cloud processing
  • point cloud registration, segmentation, object detection, semantic labelling, compression and quality assessment
  • fusion of multimodal point clouds
  • modeling of LiDAR/image-based point cloud processing
  • industrial applications with large-scale point clouds
  • high-performance computing for large-scale point clouds

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Related Special Issue

Published Papers (5 papers)

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Research

24 pages, 2196 KB  
Article
MOT-Assisted Object-Level Point Cloud Extraction from Multi-View Observations
by Cheng Ju, Zejing Zhao, Chaowen Shen, Xuantong Li and Akio Namiki
Sensors 2026, 26(16), 5032; https://doi.org/10.3390/s26165032 - 7 Aug 2026
Viewed by 339
Abstract
Object-level point cloud extraction is relevant to robotic perception and may provide useful object-centric observations for mapping and SLAM. Many existing methods rely on per-frame instance segmentation, resulting in high computational cost and limited ability to extract multiple objects simultaneously in multi-object scenarios. [...] Read more.
Object-level point cloud extraction is relevant to robotic perception and may provide useful object-centric observations for mapping and SLAM. Many existing methods rely on per-frame instance segmentation, resulting in high computational cost and limited ability to extract multiple objects simultaneously in multi-object scenarios. This paper proposes an efficient MOT-assisted pipeline for multi-view object point cloud extraction. The pipeline first employs 2D multi-object tracking (MOT) to establish consistent object correspondences across views, and then combines monocular depth-based reconstruction with multi-view geometric association to estimate coarse object locations. An adaptive spherical proposal and a density-based refinement strategy are further introduced to extract clean object-specific point clouds while suppressing background noise and outliers. Experiments on the DTU, MVImgNet, and ScanNet++ datasets demonstrate the effectiveness of the proposed method. Relative to the unprocessed scene-level point cloud, the Chamfer Distance is reduced from 90.97 mm to 12.52 mm and Precision increases from 0.2998 to 0.8776 on DTU dataset Sequence 30. On MVImgNet, the Chamfer Distance decreases from 1.23 m to 0.46 m and Precision increases from 0.4777 to 0.9337, demonstrating effective removal of non-target scene points under real-world viewing conditions. Moreover, the proposed method reduces the cost of object-level association and provides competitive object-level extraction quality under the tested settings. Compared with the closely matched segmentation-based extraction, the proposed method provides lower measured object extraction runtime and built-in cross-view Track-ID association, while sacrificing some boundary accuracy. Full article
(This article belongs to the Special Issue Intelligent Point Cloud Processing, Sensing and Understanding—Fourth Edition)
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20 pages, 8638 KB  
Article
Dual-Expert Landmark Localization in 3D Facial Point Clouds Under Controlled Synthetic Local Surface Loss for Rigid Initialization
by Zichao Zou, Zongjian Chen, Rongqian Yang, Kehai Peng and Shizhong Jiang
Sensors 2026, 26(15), 4989; https://doi.org/10.3390/s26154989 - 6 Aug 2026
Viewed by 344
Abstract
Local surface loss can destabilize landmark-based rigid initialization in three-dimensional (3D) facial point clouds. We propose a dual-expert framework for localizing five anatomical landmarks under controlled synthetic surface loss. The Clean expert is optimized for peak-based localization on complete surfaces, whereas the Occlusion [...] Read more.
Local surface loss can destabilize landmark-based rigid initialization in three-dimensional (3D) facial point clouds. We propose a dual-expert framework for localizing five anatomical landmarks under controlled synthetic surface loss. The Clean expert is optimized for peak-based localization on complete surfaces, whereas the Occlusion expert combines local coordinate regression, visibility estimation, heteroscedastic modeling, and a global structural prior. A model-output reliability gate removes dependence on the protocol-supplied surface-loss ratio. For scenes not classified as reliably complete, the Occlusion expert provides the default and fallback prediction, while a validation-selected Clean residual is applied only under landmark-wise agreement. On a subject-independent FaceScape test set, mean localization errors were 0.470±0.688, 1.159±1.093, and 2.216±2.169 mm at 0%, 30%, and 50% surface loss. The method significantly outperformed the Unified occlusion-aware and structure-robust (OASR) model and the Occlusion expert at 30% and 50% loss after Holm correction and achieved lower error than Unified OASR in 22 of 24 structured-corruption conditions. In a controlled large-pose stress test, initialization achieved 100% iterative closest point (ICP) success versus 98.82% for the two-stage stratified graph convolutional network (2S-SGCN) baseline and improved coarse alignment, whereas post-ICP accuracy did not differ significantly. These results support robust rigid initialization under controlled synthetic surface loss on FaceScape; generalization to real sensor-acquired point clouds remains to be established. Full article
(This article belongs to the Special Issue Intelligent Point Cloud Processing, Sensing and Understanding—Fourth Edition)
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19 pages, 2860 KB  
Article
Structure-Preserving Point Cloud Completion with Symmetry-Guided Progressive Refinement
by Shuanfeng Zhao and Yixin Niu
Sensors 2026, 26(11), 3536; https://doi.org/10.3390/s26113536 - 3 Jun 2026
Viewed by 389
Abstract
Point cloud completion from partial observations remains challenging due to the trade-off between preserving global structural consistency and recovering fine-grained local details, especially under severe incompleteness. We propose a symmetry-guided progressive refinement network to address this problem by learning flexible structural correspondences and [...] Read more.
Point cloud completion from partial observations remains challenging due to the trade-off between preserving global structural consistency and recovering fine-grained local details, especially under severe incompleteness. We propose a symmetry-guided progressive refinement network to address this problem by learning flexible structural correspondences and progressively refining incomplete shapes. First, a Symmetry Graph Inference Network (SymGraphNet) constructs a feature-space graph over sampled keypoints and predicts symmetry-guided structural counterparts for robust coarse shape recovery, without explicitly estimating a rigid symmetry plane or axis. Second, a confidence-weighted Cross-Aware Decoder adaptively fuses partial-observation features and symmetry-guided features to balance visible-region fidelity and missing-region completion. Third, a multi-stage residual refinement strategy progressively improves geometric fidelity, local continuity, and point distribution uniformity. Experiments on PCN, MVP, and KITTI datasets demonstrate consistent improvements over representative state-of-the-art methods under both synthetic and real-world incomplete point cloud settings. Full article
(This article belongs to the Special Issue Intelligent Point Cloud Processing, Sensing and Understanding—Fourth Edition)
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22 pages, 2387 KB  
Article
Dynamic Occlusion–Predictive Neural Network for Robust Roadside Multi-Vehicle Tracking
by Shuai Wang, Yafei Wang, Bowen Wang, Chongfeng Wei and Hao Liu
Sensors 2026, 26(11), 3529; https://doi.org/10.3390/s26113529 - 2 Jun 2026
Viewed by 553
Abstract
Despite their extended detection ranges and superior precision compared with onboard sensors, roadside perception systems suffer from severe occlusion artifacts in complex traffic, causing significant tracking failures and ID switches. To address this, we propose a novel Dynamic Occlusion–Predictive Neural Network tailored to [...] Read more.
Despite their extended detection ranges and superior precision compared with onboard sensors, roadside perception systems suffer from severe occlusion artifacts in complex traffic, causing significant tracking failures and ID switches. To address this, we propose a novel Dynamic Occlusion–Predictive Neural Network tailored to challenging roadside environments. First, we introduce a Transformer-based Dynamic Occlusion State Predictor to explicitly model the temporal evolution of occlusion. Unlike traditional tracking methods, this module continuously forecasts future occlusion ratios for each target by analyzing historical occlusion patterns. Critically, these predictions are integrated into the tracking framework as dynamic weighting factors in the loss function, enabling the model to adaptively penalize tracking errors based on the predicted occlusion severity and significantly enhancing robustness against dynamic occlusion scenarios. Second, leveraging the predicted occlusion states, we propose a GNN-based Spatial Reasoning Module to address trajectory fragmentation. This module constructs a heterogeneous graph integrating road occupancy information and neighboring vehicle poses to infer the existence and motion patterns of targets within occluded regions. By analyzing scene-level physical constraints, it generates motion predictions for invisible targets and links these inferred states to fragmented trajectories, ensuring temporally continuous tracking even during prolonged visual occlusions. Experiments on the DAIR-V2X and our self-collected roadside dataset show that our framework outperforms state-of-the-art methods in precision and robustness, achieving a 5.1% MOTA gain over the best baseline. This advantage peaks under high occlusion, where preserving ID continuity and minimizing failures validates its efficacy for real-world roadside multi-target tracking. Full article
(This article belongs to the Special Issue Intelligent Point Cloud Processing, Sensing and Understanding—Fourth Edition)
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25 pages, 49356 KB  
Article
Distillation Style Regulators and Semantic Prior-Guided Framework for Non-Ideal Single-View 3D Vehicle Point Cloud Reconstruction
by Jinghao Cao, Xiajun Liu and Rui Xue
Sensors 2026, 26(11), 3359; https://doi.org/10.3390/s26113359 - 26 May 2026
Viewed by 438
Abstract
The closed-loop testing of autonomous driving systems critically depends on large-scale libraries of diverse and realistic 3D vehicle assets, yet current pipelines still rely on labor-intensive modeling or multi-view capture, making efficient construction a key bottleneck. To overcome this bottleneck and enable convenient, [...] Read more.
The closed-loop testing of autonomous driving systems critically depends on large-scale libraries of diverse and realistic 3D vehicle assets, yet current pipelines still rely on labor-intensive modeling or multi-view capture, making efficient construction a key bottleneck. To overcome this bottleneck and enable convenient, cost-effective 3D asset generation, we propose a semantic prior-guided framework for accurate and robust vehicle point cloud reconstruction from casually captured single-view photographs. Our framework is built on a diffusion backbone but is fundamentally driven by two forms of prior knowledge: First, geometric and appearance priors from camera-aware image features, masks, and distance-transform maps are projected onto the evolving point cloud, compensating for the severe information loss in single-view inputs. Second, we introduce distillation-style regulators—pretrained neural networks that encode vehicle type and model semantics; they act as teacher networks that impose high-level constraints on the generated point clouds, transferring rich semantic knowledge and effectively regularizing the learning process. With these priors, our model infers vehicle-specific semantics from limited observations and reconstructs high-quality 3D point cloud assets. On the 3DRealCar++ dataset, our method clearly surpasses state-of-the-art point cloud baselines in both F-score and Chamfer Distance. Full article
(This article belongs to the Special Issue Intelligent Point Cloud Processing, Sensing and Understanding—Fourth Edition)
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