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.

Preprints.org is a multidisciplinary platform offering a preprint service designed to facilitate the early sharing of your research. It supports and empowers your research journey from the very beginning.

MDPI Topics is collaborating with Preprints.org and has established a direct connection between MDPI journals and the platform. Authors are encouraged to take advantage of this opportunity by posting their preprints at Preprints.org prior to publication:

1. Share your research immediately: disseminate your ideas prior to publication and establish priority for your work.
2. Safeguard your intellectual contribution: Protect your ideas with a time-stamped preprint that serves as proof of your research timeline.
3. Boost visibility and impact: Increase the reach and influence of your research by making it accessible to a global audience.
4. Gain early feedback: Receive valuable input and insights from peers before submitting to a journal.
5. Ensure broad indexing: Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Related Topic

Published Papers (5 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

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

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

25 pages, 1689 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

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)

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 298

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)

►▼ Show Figures

Figure 1

19 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

Viewed by 277

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)

►▼ Show Figures

Figure 1

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 411

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)

►▼ Show Figures

Figure 1

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 867

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)

►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-5