3D Computer Vision and Smart Building and City, 4th Edition

Topic Information

Dear Colleagues,

We are pleased to announce the fourth edition of the successful Topic “3D Computer Vision and Smart Building and City”.

Three-dimensional computer vision is an interdisciplinary subject involving computer graphics, artificial intelligence, and other fields. Its main contents include 3D perception, 3D understanding, and 3D modeling. In recent years, 3D computer vision technology has developed rapidly and has been widely used in unmanned aerial vehicles, robots, autonomous driving, AR, VR, and other fields. Smart buildings and cities use various information technologies or innovative concepts to connect various systems and services to improve the efficiency of resource utilization, optimize management and services, and improve quality of life. Smart buildings and cities can involve some new techniques, such as 3D CV for building information models, digital twins, city information models, simultaneous localization and mapping, and robots. The application of 3D computer vision in smart buildings and cities is a valuable research direction, but it still faces many major challenges in theory and technology. This Topic focuses on the theory and technology of 3D computer vision in smart buildings and cities. We invite the research community to publish papers and provide innovative technologies, theories, or case studies.

Prof. Dr. Junxing Zheng
Dr. Peng Cao
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.9	6.1	2011	15 Days	CHF 2400	Submit
Buildings buildings	3.4	5.6	2011	14.7 Days	CHF 2600	Submit
CivilEng civileng	2.8	4.4	2020	23.5 Days	CHF 1400	Submit
Designs designs	-	5.7	2017	19.5 Days	CHF 1600	Submit
Intelligent Infrastructure and Construction iic	-	-	2025	15.0 days *	CHF 1000	Submit
ISPRS International Journal of Geo-Information ijgi	3.2	6.7	2012	34.9 Days	CHF 1900	Submit
Sensors sensors	4.0	9.4	2001	17.8 Days	CHF 2600	Submit

* Median value for all MDPI journals in the first half of 2026.

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Published Papers (8 papers)

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All Applied Sciences Buildings CivilEng Designs IJGI Intelligent Infrastructure and Construction Sensors

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32 pages, 7102 KB  

Open AccessArticle

Multiscale Quantification of Visual Morphology in Modern Historic Buildings: A Computer Vision Study of the Five Former Concessions in Hankou

by Zi Wang and Wei Shang

Buildings 2026, 16(19), 3938; https://doi.org/10.3390/buildings16193938 - 3 Oct 2026

Viewed by 111

Abstract

Modern historic buildings encode exchanges among social development, regional culture, and construction technology. The surviving buildings in the five former concessions of Hankou display diverse facade compositions and window, door, and column forms, but conventional typological analysis depends heavily on expert judgement and [...] Read more.

Modern historic buildings encode exchanges among social development, regional culture, and construction technology. The surviving buildings in the five former concessions of Hankou display diverse facade compositions and window, door, and column forms, but conventional typological analysis depends heavily on expert judgement and is difficult to scale. This study developed a traceable three-level Building–Facade–Component dataset comprising 59 buildings, 77 facades, and 2456 components. At the facade scale, 18 OpenCV-derived morphological descriptors were evaluated first at the facade level and subsequently after multiple facades were aggregated within each Building ID. Building-level permutation tests with false-discovery-rate correction showed that none of the 18 descriptors provided stable evidence for concession-group differences. At the component scale, frozen ResNet50 representations were reduced using principal component analysis and clustered using K-means, while UMAP was used only for exploratory visualization; an archived DINOv2 feature matrix was examined as a secondary representation-sensitivity comparison. The selected partitions yielded three window, two door, and two column cluster-informed visual groupings without component-level reassignment. Independent blinded semantic evaluation showed limited concordance for the three window descriptions (53.4%; κ = 0.296), but stronger concordance for the door (87.3%; κ = 0.750) and column groupings (91.3%; κ = 0.825). Component-level frequencies showed descriptive variation among the sampled concessions; however, building-level composition tests and building-clustered permutation analyses did not support stable concession-related associations. The framework therefore provides complementary, scale-dependent evidence for structured heritage documentation and exploratory morphological comparison while preserving traceability to the original buildings, facades, and component images. Full article

(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 4th Edition)

20 pages, 17900 KB  

Open AccessArticle

MHF-Net: Multi-Modal Residual Fusion Object Detection Network Based on High-Frequency Gated Convolution

by Bailin Chen and Bo Qian

Sensors 2026, 26(18), 5816; https://doi.org/10.3390/s26185816 - 14 Sep 2026

Viewed by 291

Abstract

This paper aims to address image degradation caused by adverse weather conditions and partial occlusion in autonomous driving scenarios. The network adopts a dual-branch architecture, which utilizes a residual fusion block (ResFuse) to achieve the deep fusion of cross-modal features. A dual-path attention [...] Read more.

This paper aims to address image degradation caused by adverse weather conditions and partial occlusion in autonomous driving scenarios. The network adopts a dual-branch architecture, which utilizes a residual fusion block (ResFuse) to achieve the deep fusion of cross-modal features. A dual-path attention mechanism (DualAtt) and an integrated high-frequency attention module (HFAtt) were designed to capture key information in feature maps. Additionally, this study designs a spatial-channel downsampling module (SCDown). This module implements spatial-to-channel image downsampling. Experimental results demonstrate that the proposed network achieves a detection speed of 61 frames per second (FPS) and precision (P) of up to 87.4% on the M3FD dataset. Comparative experimental results show that MHF-Net achieves 84.3% in the mAP₅₀ evaluation metric. Compared to the baseline model, the number of parameters was reduced by 95% and the computational complexity was reduced by 88%. Compared with other mainstream object detection algorithms, MHF-Net strikes a favorable balance regarding performance in autonomous driving scenarios. Full article

(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 4th Edition)

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22 pages, 6306 KB  

Open AccessArticle

Mamba-BEV: A Multiscale State-Space Framework for 3D Object Detection from Point Clouds

by Yuyang Liu, Jiabin Wang, Min Mao, Kun Zhang, Yu Xu, Mingchen Zhu and Xianjun Wu

Sensors 2026, 26(16), 5271; https://doi.org/10.3390/s26165271 - 20 Aug 2026

Viewed by 381

Abstract

LiDAR point clouds are sparse, irregular, and unevenly distributed, which makes representative feature extraction challenging for 3D object detection. Currently, Mamba modules have been increasingly applied to 3D object detection due to their ability to efficiently model global spatial dependencies. However, preserving geometric [...] Read more.

LiDAR point clouds are sparse, irregular, and unevenly distributed, which makes representative feature extraction challenging for 3D object detection. Currently, Mamba modules have been increasingly applied to 3D object detection due to their ability to efficiently model global spatial dependencies. However, preserving geometric structures while modeling global spatial dependencies remains challenging in Mamba-based frameworks. In view of this, this paper proposes a single-stage 3D object detection framework for LiDAR-based point clouds. First, this paper designs a hierarchical multiscale structure called Multiscale Voxel–Point Alternating Fusion (MVPF) Module. Within this module, the 2DMamba module is introduced into the feature extraction stage at each scale to model global spatial dependencies in the BEV plane through selective state-space scanning. Second, we design a Z-to-Channel cross-dimensional reorganization strategy that merges the voxel Z dimension into the channel dimension, yielding a BEV-form feature representation suitable for 2DMamba processing. Third, this paper proposes a Local Voxel Feature Enhancement (LVFE) module composed of a Point-to-Voxel Feature Aggregation (PVFA) module, a Voxel Densification Module (VDM), and a coordinate-indexed Voxel-to-Point Mapping (VPM) module, which enhances local voxel representations while maintaining point–voxel spatial correspondence. On the KITTI validation set, Mamba-BEV achieved higher detection accuracy and inference speed than the baseline. Under the Moderate difficulty level of AP_R11, the 3D AP for Car, Pedestrian, and Cyclist improved by 0.6%, 1.5%, and 0.4%, respectively, compared to the baseline, while the inference speed increased from 44.05 FPS to 67.11 FPS. These results demonstrate the effectiveness of the proposed Mamba-BEV for point cloud-based 3D object detection. Full article

(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 4th Edition)

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25 pages, 9999 KB  

Open AccessArticle

Lightweight 3DGS-SLAM for Memory-Constrained Environments: Spatial-Aware Truncation and Adaptive Antihallucination Restoration Mechanism

by Honghui Fan, Zikai Li, Hongjin Zhu and Wenhe Chen

ISPRS Int. J. Geo-Inf. 2026, 15(7), 306; https://doi.org/10.3390/ijgi15070306 - 6 Jul 2026

Viewed by 630

Abstract

Dense simultaneous localization and mapping (SLAM) via 3D Gaussian splatting (3DGS) faces memory bottlenecks due to the explosive growth of primitives during long-sequence mapping. We propose SATA-SLAM, a framework featuring spatial-aware truncation and adaptive anti-hallucination. The online front-end maintains a constant memory footprint [...] Read more.

Dense simultaneous localization and mapping (SLAM) via 3D Gaussian splatting (3DGS) faces memory bottlenecks due to the explosive growth of primitives during long-sequence mapping. We propose SATA-SLAM, a framework featuring spatial-aware truncation and adaptive anti-hallucination. The online front-end maintains a constant memory footprint via a spatial-aware pruning module (SAPM), which employs a survival scoring function that couples primitive opacity with view-frustum projection coverage and a temporal protection window. Subsequently, an anti-hallucination generative refinement module (AGRM) utilizes texture priors from pretrained diffusion models for offline inpainting of residual regions. In addition, an adaptive gating mechanism to verify and suppress AIGC-induced hallucinations caused by pose drift, ensuring multiview consistency. Experiments on the public Replica dataset show that SATA-SLAM improves rendering quality from 12.5 dB to 37.44 dB (averaged over the Replica room0 and office0 scenes) while using only 26% of the original memory, outperforming the unconstrained baseline. This study provides a pathway toward low-power, high-fidelity environmental perception for mobile robots. Full article

(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 4th Edition)

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34 pages, 2054 KB  

Open AccessReview

A Comprehensive Survey on Diffusion Model-Driven 3D Reconstruction: Methods, Datasets, and Prospects

by Qianwen Yao, Caixia Liu, Jiulin Liang, Haisheng Li, Xiaoqun Wu and Xiaoqiang Teng

ISPRS Int. J. Geo-Inf. 2026, 15(7), 293; https://doi.org/10.3390/ijgi15070293 - 1 Jul 2026

Viewed by 1231

Abstract

Three-dimensional (3D) reconstruction serves as a key technology bridging the real and digital worlds, with broad application in remote sensing, autonomous driving, and robotics. In recent years, its technical paradigm has shifted from geometry-based methods to data-driven approaches, with diffusion models emerging as [...] Read more.

Three-dimensional (3D) reconstruction serves as a key technology bridging the real and digital worlds, with broad application in remote sensing, autonomous driving, and robotics. In recent years, its technical paradigm has shifted from geometry-based methods to data-driven approaches, with diffusion models emerging as a major driving force due to their stable training process, strong ability to learn priors from large-scale datasets, and excellent controllability over outputs. Despite the proliferation of diverse architectures, a systematic analysis and comparison of these methods remains absent. In this paper, we present a comprehensive review of diffusion-based 3D reconstruction methods. Based on the space where diffusion operates, we first categorize existing approaches into four types—image diffusion, latent diffusion, 3D diffusion, and hybrid diffusion—and we provide a detailed analysis of their methodologies. We then summarize commonly used 3D datasets and provide a comparative evaluation of these methods across three dimensions: reconstruction accuracy, computational efficiency, and generalization capabilities. Finally, we discuss future developments of diffusion-based 3D reconstruction methods. Full article

(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 4th Edition)

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18 pages, 4288 KB  

Open AccessArticle

LSCA-RCNN: Large-Kernel Spatial Residual and Cascade Attention Network for Voxel-Based 3D Object Detection

by Yuyang Liu, Zhanyuan Jiang, Min Mao, Kun Zhang, Yu Xu, Mingchen Zhu and Xianjun Wu

Sensors 2026, 26(13), 4089; https://doi.org/10.3390/s26134089 - 27 Jun 2026

Cited by 1 | Viewed by 679

Abstract

LiDAR-based 3D object detection remains challenging due to sparse and irregular point cloud distributions, which degrade detection accuracy for small and occluded objects. In view of this, this paper proposes a novel two-stage voxel-based 3D detector, namely LSCA-RCNN, to address these issues. First, [...] Read more.

LiDAR-based 3D object detection remains challenging due to sparse and irregular point cloud distributions, which degrade detection accuracy for small and occluded objects. In view of this, this paper proposes a novel two-stage voxel-based 3D detector, namely LSCA-RCNN, to address these issues. First, spatial residual blocks (SRBs) and large-kernel spatial-wise convolutions are integrated into the 3D backbone to suppress feature degradation and to expand the receptive fields for stable multi-scale feature learning. Second, a ConvNeXt-based 2D backbone with spatial attention is constructed to enhance discriminative feature representation of small objects. Third, a cascaded detection head embedded with fine-grained grouped convolutions and cross-stage cross-attention is designed to achieve progressive bounding box refinement and to improve localization precision. Extensive evaluations on the KITTI dataset with the R40 metric show that the proposed method achieves consistent performance improvements over the baseline. In the moderate setting, LSCA-RCNN increases the 3D AP by 2.12%, 7.66%, and 5.43% for cars, pedestrians, and cyclists, respectively, while achieving gains of 1.62%, 5.05%, and 7.05% under the hard setting. These results validate the effectiveness and robustness of the proposed LSCA-RCNN for complex and challenging autonomous driving detection tasks. Full article

(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 4th Edition)

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32 pages, 27890 KB  

Open AccessArticle

Serverless 3D Reconstruction and Spatial Anchoring for Cloud-Native Infrastructure Inspection

by Youssef Arhrib, Flor Alvarez-Taboada and Hakim Boulaassal

Buildings 2026, 16(12), 2433; https://doi.org/10.3390/buildings16122433 - 18 Jun 2026

Viewed by 804

Abstract

While infrastructure asset management increasingly relies on high-resolution drone imagery, existing workflows suffer from fragmented information management and dependence on costly local processing infrastructure. This paper addresses these limitations by using a cloud-native spatial intelligence hub that converts raw inspection imagery into an [...] Read more.

While infrastructure asset management increasingly relies on high-resolution drone imagery, existing workflows suffer from fragmented information management and dependence on costly local processing infrastructure. This paper addresses these limitations by using a cloud-native spatial intelligence hub that converts raw inspection imagery into an interactive and queryable three-dimensional information layer. The system integrates a timeout-resilient orchestration layer for photogrammetry pipelines, a multi-user three-dimensional environment for collaborative review, and a PostGIS-backed spatial database that stores defects as georeferenced anchors. We further introduce a spatial anchoring workflow mapping three-dimensional interactions to world coordinates, retrieving context-relevant images via frustum-based visibility scoring. Evaluated on real inspection datasets, the serverless architecture achieved end-to-end reconstruction in under one hour with sub-25 ms query latency. Results indicate that acquisition geometry, particularly oblique convergent viewpoints, is a stronger predictor of reconstruction complexity than image count. This work establishes a reproducible reference architecture, enabling a transition from file-centric documentation to traceable, spatially indexed evidence management for infrastructure Digital Twins. Full article

(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 4th Edition)

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42 pages, 15306 KB  

Open AccessArticle

A Closed-Loop Framework for Tunnel Blasting Optimization Using Multi-View 3D Reconstruction and Intelligent Recognition

by Jianjun Shi, Jiayi Sun, Wenxin Shan, Yongsheng Jia, Yingkang Yao and Hongsheng Wang

ISPRS Int. J. Geo-Inf. 2026, 15(6), 237; https://doi.org/10.3390/ijgi15060237 - 26 May 2026

Viewed by 1369

Abstract

The assessment of tunnel blasting effects traditionally relies on manual inspection and contact measurements, which are subjective, inefficient, and lack comprehensive quantification. To address this, this study proposes a novel closed-loop framework that integrates multi-view 3D reconstruction with intelligent recognition for quantitative blasting [...] Read more.

The assessment of tunnel blasting effects traditionally relies on manual inspection and contact measurements, which are subjective, inefficient, and lack comprehensive quantification. To address this, this study proposes a novel closed-loop framework that integrates multi-view 3D reconstruction with intelligent recognition for quantitative blasting evaluation and parameter optimization. Rather than claiming novelty in these basic computer vision algorithms, the novelty of this work lies in their tunnel blasting oriented integration: reconstructed geometry is converted into blasting relevant indicators and then linked to parameter adjustment decisions within a closed-loop workflow. The framework begins with a standardized image acquisition workflow designed for challenging tunnel environments (e.g., dust, uneven light), followed by image enhancement using histogram equalization and bilateral filtering. A key improvement is an enhanced SIFT feature matching strategy, which incorporates a BBF optimized K-D tree and RANSAC to achieve robust correspondence establishment on texture-repetitive rock surfaces. This enables the generation of high-precision 3D models of the tunnel face via Structure from Motion (SfM) and Poisson surface reconstruction. From these models, quantitative indices are automatically extracted: rock mass structural planes are clustered via the ISODATA algorithm, structural traces are delineated using a minimum cost path method, and face flatness is evaluated through curvature analysis. These indices form the basis for intelligent blasting assessment. Crucially, the assessment results are directly fed back to optimize blasting parameters (e.g., adding cut holes, adjusting auxiliary hole spacing). Field application in the Huangtai Tunnel demonstrated that this closed-loop framework significantly improved face flatness (achieving over 50% improvement in the high-curvature area ratio) and contour control. Further verification in the Donghongshan Tunnel showed that the proportion of the sharp feature region decreased from 20.3% to 7.9% after optimization. The proposed framework transitions blasting management from empirical judgment to a data driven, intelligent optimization process, offering a scalable solution for enhancing quality and efficiency in tunnel construction. Full article

(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 4th Edition)

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