{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T11:19:10Z","timestamp":1772450350627,"version":"3.50.1"},"reference-count":121,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T00:00:00Z","timestamp":1762473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>This article presents a comprehensive forensic tool for crime scene and traffic accident investigations, integrating advanced 3D reconstruction and semantic and dynamic analyses; the tool facilitates the accurate documentation and preservation of crime scenes through photogrammetric techniques, producing detailed 3D models based on images or video captured under specified protocols. The system includes modules for semantic analysis, enabling object detection and classification in 3D point clouds and 2D images. By employing machine learning methods such as the Random Forest model for point cloud classification and the YOLOv8 architecture for object detection, the tool enhances the accuracy and reliability of forensic analysis. Furthermore, a dynamic analysis module supports ballistic trajectory calculations for crime scene investigations and the vehicle impact speed estimation using the Equivalent Barrier Speed (EBS) model for traffic accidents. These capabilities are integrated into a single, user-friendly platform offering significant improvements over existing forensic tools, which often focus on singular tasks and require expertise. This tool provides a robust, accessible solution for law enforcement agencies, enabling more efficient and precise forensic investigations across different scenarios.<\/jats:p>","DOI":"10.3390\/a18110707","type":"journal-article","created":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T10:56:45Z","timestamp":1762513005000},"page":"707","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Comprehensive Forensic Tool for Crime Scene and Traffic Accident 3D Reconstruction"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1103-9966","authenticated-orcid":false,"given":"Alejandra","family":"Ospina-Boh\u00f3rquez","sequence":"first","affiliation":[{"name":"Department of Cartographic and Land Engineering, Higher Polytechnic School of \u00c1vila, Universidad de Salamanca, Hornos Caleros, 50, 05003 \u00c1vila, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6342-0821","authenticated-orcid":false,"given":"Esteban","family":"Ruiz de O\u00f1a","sequence":"additional","affiliation":[{"name":"Department of Cartographic and Land Engineering, Higher Polytechnic School of \u00c1vila, Universidad de Salamanca, Hornos Caleros, 50, 05003 \u00c1vila, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4542-3755","authenticated-orcid":false,"given":"Roy","family":"Yali","sequence":"additional","affiliation":[{"name":"Department of Cartographic and Land Engineering, Higher Polytechnic School of \u00c1vila, Universidad de Salamanca, Hornos Caleros, 50, 05003 \u00c1vila, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7947-0657","authenticated-orcid":false,"given":"Emmanouil","family":"Patsiouras","sequence":"additional","affiliation":[{"name":"Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece"}]},{"given":"Katerina","family":"Margariti","sequence":"additional","affiliation":[{"name":"CERIDES, European University Cyprus, Nicosia 1516, Cyprus"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8949-4216","authenticated-orcid":false,"given":"Diego","family":"Gonz\u00e1lez-Aguilera","sequence":"additional","affiliation":[{"name":"Department of Cartographic and Land Engineering, Higher Polytechnic School of \u00c1vila, Universidad de Salamanca, Hornos Caleros, 50, 05003 \u00c1vila, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,7]]},"reference":[{"key":"ref_1","unstructured":"Stuart, H.J., Nordby, J.J., and Bell, S. (2002). Forensic Science: An Introduction to Scientific and Investigative Techniques, CRC Press."},{"key":"ref_2","unstructured":"Docchio, F., Sansoni, G., Tironi, M., and Bui, C. (2006, January 11\u201313). Sviluppo di procedure di misura per il rilievo ottico tridimensionale di scene del crimine. Proceedings of the XXIII Congresso Nazionale Associazione Gruppo di Misure Elettriche ed Elettroniche, L\u2019Aquila, Italy."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1016\/j.bjps.2005.10.025","article-title":"Three-dimensional recording of the human face with a 3D laser scanner","volume":"59","author":"Kovacs","year":"2006","journal-title":"J. Plast. Reconstr. Aesthetic Surg."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Cavagnini, G., Sansoni, G., and Trebeschi, M. (2009, January 2\u20136). Using 3D range cameras for crime scene documentation and legal medicine. Proceedings of the the SPIE\u2014The International Society for Optical Engineering, San Diego, CA, USA.","DOI":"10.1117\/12.806191"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"568","DOI":"10.3390\/s90100568","article-title":"State-of-The-Art and Applications of 3D Imaging Sensors in Industry, Cultural Heritage, Medicine, and Criminal Investigation","volume":"9","author":"Sansoni","year":"2009","journal-title":"Sensors"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/S0950-7051(03)00033-9","article-title":"Extracting relational facts for indexing and retrieval of crime-scene photographs","volume":"16","author":"Pastra","year":"2003","journal-title":"Knowl.-Based Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1376","DOI":"10.1111\/j.1556-4029.2009.01170.x","article-title":"Forensic terrestrial photogrammetry from a single image","volume":"54","year":"2009","journal-title":"Forensic Sci."},{"key":"ref_8","unstructured":"D\u2019Apuzzo, N., and Harvey, M. (2008). Medical applications. Advances in Photogrammetry, Remote Sensing and Spatial Information Sciences: 2008 ISPRS Congress Book, CRC Press."},{"key":"ref_9","first-page":"355","article-title":"Integration of Laser Scanning and Photogrammetry","volume":"36","author":"Honkavaara","year":"2006","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci"},{"key":"ref_10","unstructured":"El-Hakim, S., Beraldin, J.-A., and Blais, F. (2003, January 4\u20138). A Comparative Evaluation of the Performance of Passive and Active 3-D Vision Systems. Proceedings of the SPIE\u2014The International Society for Optical Engineering, San Diego, CA, USA."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Remondino, F., Guarnieri, A., and Vettore, A. (2004, January 12\u201316). 3D modeling of Close-Range Objects: Photogrammetry or Laser Scanning. Proceedings of the SPIE, Kissimmee, FL, USA.","DOI":"10.1117\/12.586294"},{"key":"ref_12","first-page":"266","article-title":"Digital camera calibration methods: Considerations and comparisons","volume":"36","author":"Remondino","year":"2005","journal-title":"Ine. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_13","unstructured":"(2024, August 05). Apero-Micmac Open Source Tools. Available online: http:\/\/www.tapenade.gamsau.archi.fr\/TAPEnADe\/Tools.html."},{"key":"ref_14","unstructured":"(2024, August 05). Cloud Compare Open Source Tool. Available online: http:\/\/www.danielgm.net\/cc\/."},{"key":"ref_15","first-page":"269","article-title":"APERO, an open source bundle adjusment software for automatic calibration and orientation of set of images","volume":"XXXVIII-5\/W16","author":"Deseilligny","year":"2011","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_16","first-page":"549","article-title":"Close-range photogrammetric tools for small 3D archeological objects","volume":"XL-5\/W2","author":"Samaan","year":"2013","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1145\/3503250","article-title":"NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis","volume":"65","author":"Mildenhall","year":"2021","journal-title":"Commun. ACM"},{"key":"ref_18","first-page":"2757","article-title":"Opportunities for utilizing consumer grade 3D capture tools for insurance documentation","volume":"14","author":"Ponto","year":"2022","journal-title":"Int. J. Inf. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Martin-Brualla, R., Radwan, N., Sajjadi, M.S.M., Barron, J.T., Dosovitskiy, A., and Duckworth, D. (2021, January 20\u201325). NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections. Proceedings of the Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00713"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Pumarola, A., Corona, E., Pons-Moll, G., and Moreno-Noguer, F. (2021, January 20\u201325). D-NeRF: Neural Radiance Fields for Dynamic Scenes. Proceedings of the Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01018"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tretschk, E., Tewari, A., Golyanik, V., Zollh\u00f6fer, M., Lassner, C., and Theobalt, C. (2021, January 20\u201325). Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video. Proceedings of the Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/ICCV48922.2021.01272"},{"key":"ref_22","unstructured":"Wang, Z., Wu, S., Xie, W., Chen, M., and Prisacariu, V.A. (2021, January 20\u201325). NeRF--: Neural Radiance Fields Without Known Camera Parameters. Proceedings of the Computer Vision and Pattern Recognition, Nashville, TN, USA."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhu, H., Wu, W., Zhu, W., Jiang, L., Tang, S., Zhang, L., Liu, Z., and Loy, C.C. (2022, January 23\u201327). CelebV-HQ: A Large-Scale Video Facial Attributes Dataset. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-20071-7_38"},{"key":"ref_24","first-page":"27","article-title":"Investigaci\u00f3n & Reconstrucci\u00f3n de accidentes: La Reconstrucci\u00f3n pr\u00e1ctica de un accidente de tr\u00e1fico","volume":"1","year":"2011","journal-title":"Secur. Vialis"},{"key":"ref_25","first-page":"109","article-title":"La reconstrucci\u00f3n de accidentes desde el punto de vista policial","volume":"1","author":"Andreu","year":"2004","journal-title":"Cuad. Guard. Civ. Rev. Segur. P\u00fablica"},{"key":"ref_26","unstructured":"Carballo, H. (2005). Pericias Tecnico-Mecanicas, Ediciones Larocca."},{"key":"ref_27","unstructured":"Robsan, S., Kyle, S., and Harley, I. (2011). Close Range Photogrammetry: Principles, Techniques and Applications, Whittles Publishing."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1016\/j.measurement.2012.04.021","article-title":"Accuracy assessment of vehicles surface area measurement by means of statistical methods","volume":"46","year":"2013","journal-title":"Measurement"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/j.advengsoft.2008.09.002","article-title":"Geometry features measurement of traffic accident for reconstruction based on close-range photogrammetry","volume":"40","author":"Du","year":"2009","journal-title":"Adv. Eng. Softw."},{"key":"ref_30","first-page":"115","article-title":"Close-range photogrammetry for accident reconstruction","volume":"Volume 2","author":"Gruen","year":"2005","journal-title":"Optical 3D Measurements VII"},{"key":"ref_31","unstructured":"Fraser, C., Cronk, S., and Hanley, H. (2008, January 3\u201311). Close-range photogrammetry in traffic incident management. Proceedings of the XXI ISPRS Congress Commission V, Beijing, China."},{"key":"ref_32","first-page":"441","article-title":"Automated procedures with coded targets in industrial vision metrology","volume":"68","author":"Hattori","year":"2002","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1177\/0954407015586906","article-title":"Determination of the collision speed of a vehicle from evaluation of the crush volume using photographs","volume":"230","author":"Han","year":"2016","journal-title":"Proc. Inst. Mech. Eng. Part D J. Automob. Eng."},{"key":"ref_34","unstructured":"Pool, G., and Venter, P. (2004, January 12\u201315). Measuring accident scenes using laser scanning systems and the use of scan data in 3D simulation and animation. Proceedings of the 23rd Annual Southern African Transport Conference, Pretoria, South Africa."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.forsciint.2006.08.024","article-title":"Application of 3D documentation and geometric reconstruction methods in traffic accident analysis: With high resolution surface scanning, radiological MSCT\/MRI scanning and real data based animation","volume":"170","author":"Buck","year":"2007","journal-title":"Forensic Sci. Int."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.forsciint.2012.05.015","article-title":"Accident or homicide\u2014Virtual crime scene reconstruction using 3D methods","volume":"225","author":"Buck","year":"2013","journal-title":"Forensic Sci. Int."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wu, T.-H., Liu, Y.-C., Huang, Y.-K., Lee, H.-Y., Su, H.-T., Huang, P.-C., and Hsu, W.H. (2021, January 11\u201317). ReDAL: Region-Based and Diversity-Aware Active Learning for Point Cloud Semantic Segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.01522"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Li, L., Sung, M., Dubrovina, A., Yi, L., and Guibas, L.J. (2019, January 16\u201320). Supervised Fitting of Geometric Primitives to 3D Point Clouds. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00276"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.isprsjprs.2018.04.018","article-title":"A probabilistic graphical model for the classification of mobile LiDAR point clouds","volume":"143","author":"Kang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Bretar, F. (2017). Feature Extraction from LiDAR Data in Urban Areas. Topographic Laser Ranging and Scanning, Productivity Press.","DOI":"10.1201\/9781420051438-14"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Li, Y., Lin, Q., Zhang, Z., Zhang, L., Chen, D., and Shuang, F. (2022). MFNet: Multi-Level Feature Extraction and Fusion Network for Large-Scale Point Cloud Classification. Remote Sens., 14.","DOI":"10.3390\/rs14225707"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"48","DOI":"10.31127\/tuje.669566","article-title":"Classification of UAV point clouds by random forest machine learning algorithm","volume":"5","author":"Zeybek","year":"2021","journal-title":"Turk. J. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"179118","DOI":"10.1109\/ACCESS.2019.2958671","article-title":"A Review of Deep Learning-Based Semantic Segmentation for Point Cloud","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_44","unstructured":"Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017, January 16\u201321). PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhao, H., Jiang, L., Jia, J., Torr, P.H., and Koltun, V. (2021, January 11\u201317). Point Transformer. Proceedings of the EEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.01595"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Landrieu, L., and Simonovsky, M. (2018, January 18\u201322). Large-Scale Point Cloud Semantic Segmentation With Superpoint Graphs. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00479"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Hu, Q., Yang, B., Xie, L., Rosa, S., Guo, Y., Wang, Z., Trigoni, N., and Markham, A. (2020, January 13\u201316). RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01112"},{"key":"ref_48","unstructured":"Thomas, H., Qi, C.R., Deschaud, J.-E., Marcotegui, B., Goulette, F., and Guibas, L.J. (November, January 27). KPConv: Flexible and Deformable Convolution for Point Clouds. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Robert, D., Raguet, H., and Landrieu, L. (2023, January 2\u20133). Efficient 3D Semantic Segmentation with Superpoint Transformer. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCV51070.2023.01577"},{"key":"ref_50","first-page":"8","article-title":"A Comprehensive Review of Image Enhancement Techniques","volume":"2","author":"Maini","year":"2010","journal-title":"J. Comput."},{"key":"ref_51","unstructured":"Verhoeven, G., Karel, W., \u0160tuhec, S., Doneus, M., Trinks, I., and Pfeifer, N. (2015, January 25\u201327). Mind your grey tones: Examining the influence of decolourization methods on interest point extraction and matching for architectural image-based modelling. Proceedings of the 3D-Arch 2015: 3D Virtual Reconstruction and Visualization of Complex Architectures, Avila, Spain."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"47","DOI":"10.5194\/isprsarchives-XL-5-47-2014","article-title":"Evaluation of feature-based methods for automated network orientation","volume":"40","author":"Apollonio","year":"2014","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.isprsjprs.2015.09.005","article-title":"Recent developments in large-scale tie-point matching","volume":"115","author":"Hartmann","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Agarwal, S., Snavely, N., Seitz, S.M., and Szeliski, R. (2010, January 5\u201311). Bundle Adjustment in the Large. Proceedings of the Computer Vision\u2013ECCV 2010: 11th European Conference on Computer Vision, Crete, Greece.","DOI":"10.1007\/978-3-642-15552-9_3"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Wu, C., Agarwal, S., Curless, B., and Seitz, S.M. (2011, January 20\u201325). Multicore bundle adjustment. Proceedings of the CVPR 2011, Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995552"},{"key":"ref_56","unstructured":"Schonberger, J.L., and Frahm, J.-M. (July, January 26). Structure-From-Motion Revisited. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1111\/phor.12063","article-title":"State of the art in high density image matching","volume":"29","author":"Remondino","year":"2014","journal-title":"Photogramm. Rec."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s11263-007-0107-3","article-title":"Modeling the World from Internet Photo Collections","volume":"80","author":"Snavely","year":"2008","journal-title":"Int. J. Comput. Vis."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Frahm, J.-M., Fite-Georgel, P., Gallup, D., Johnson, T., Raguram, R., Wu, C., Jen, Y.-H., Dunn, E., Clipp, B., and Lazebnik, S. (2010, January 5\u201311). Building Rome on a Cloudless Day. Proceedings of the Computer Vision\u2014ECCV 2010, Crete, Greece.","DOI":"10.1007\/978-3-642-15561-1_27"},{"key":"ref_60","unstructured":"Rothermel, M., Wenzel, K., Fritsch, D., and Haala, N. (2012, January 4\u20135). SURE: Photogrammetric surface reconstruction from imagery. Proceedings of the LC3D Workshop, Berlin, Germany."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Heinly, J., Schonberger, J.L., Dunn, E., and Frahm, J.-M. (2015, January 7\u201312). Reconstructing the World* in Six Days *(As Captured by the Yahoo 100 Million Image Dataset). Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298949"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Schops, T., Schonberger, J.L., Galliani, S., Sattler, T., Schindler, K., Pollefeys, M., and Geiger, A. (2017, January 21\u201326). A Multi-View Stereo Benchmark With High-Resolution Images and Multi-Camera Videos. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.272"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3072959.3073599","article-title":"Tanks and temples: Benchmarking large-scale scene reconstruction","volume":"36","author":"Knapitsch","year":"2017","journal-title":"ACM Trans. Graph. (ToG)"},{"key":"ref_64","first-page":"171","article-title":"sv3DVision: Didactical photogrammetric software for single image-based modeling","volume":"36","author":"Aguilera","year":"2006","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_65","unstructured":"Grussenmeyer, P., and Drap, P. (2001, January 23\u201327). Possibilities and limits of web photogrammetry. Proceedings of the Photogrammetric Week \u201901, Stuttgart, Germany."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1111\/phor.12001","article-title":"Virtual Worlds for Photogrammetric Image-Based Simulation and Learning","volume":"28","author":"Piatti","year":"2013","journal-title":"Photogramm. Rec."},{"key":"ref_67","unstructured":"Gonz\u00e1lez-Aguilera, D., Guerrero, D., L\u00f3pez, D.H., Rodr\u00edguez-Gonz\u00e1lez, P., Pierrot, M., and Fern\u00e1ndez-Hern\u00e1ndez, J. (September, January 25). PW, Photogrammetry Workbench. CATCON Silver Award, ISPRS WG VI\/2. Proceedings of the 22nd ISPRS Congress, Melbourne, Australia."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"39","DOI":"10.5194\/isprs-archives-XLI-B6-39-2016","article-title":"Learning Photogrammetry with Interactive Software Tool PhoX","volume":"41","author":"Luhmann","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_69","unstructured":"Wu, C. (2025, November 04). VisualSFM: A Visual Structure from Motion System. Available online: http:\/\/ccwu.me\/vsfm\/."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1362","DOI":"10.1109\/TPAMI.2009.161","article-title":"Accurate, Dense, and Robust Multiview Stereopsis","volume":"32","author":"Furukawa","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_71","unstructured":"ARC-Team Engineering srls (2025, November 03). Arc-Team. Available online: https:\/\/www.arc-team.it\/."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Waechter, M., Moehrle, N., and Goesele, M. (2014, January 6\u201312). Let There Be Color! Large-Scale Texturing of 3D Reconstructions. Proceedings of the Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_54"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.cag.2015.09.003","article-title":"MVE\u2014An image-based reconstruction environment","volume":"53","author":"Fuhrmann","year":"2015","journal-title":"Comput. Graph."},{"key":"ref_74","unstructured":"Sweeney, C. (2025, November 03). TheiaSfM. Available online: https:\/\/github.com\/sweeneychris\/TheiaSfM."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Pan, L., Bar\u00e1th, D., Pollefeys, M., and Sch\u00f6nberger, J.L. (2024, January 17\u201321). Global Structure-from-Motion Revisited. Proceedings of the Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1007\/978-3-031-73661-2_4"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1111\/phor.12231","article-title":"GRAPHOS\u2014Open-source software for photogrammetric applications","volume":"33","author":"Guerrero","year":"2018","journal-title":"Photogramm. Rec."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"565","DOI":"10.5194\/isprs-archives-XLIII-B2-2021-565-2021","article-title":"A comparison between 3D reconstruction using nerf neural networks and mvs algorithms on cultural heritage images","volume":"43","author":"Condorelli","year":"2021","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3592433","article-title":"3D Gaussian Splatting for Real-Time Radiance Field Rendering","volume":"42","author":"Kerbl","year":"2023","journal-title":"ACM Trans. Graph."},{"key":"ref_79","first-page":"1","article-title":"Application of close range photogrammetry in crime scene investigation (CSI) mapping using iwitness and crime zone software","volume":"10","author":"Aziz","year":"2010","journal-title":"Geoinf. Sci. J."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"110601","DOI":"10.1016\/j.forsciint.2020.110601","article-title":"The effects of camera resolution and distance on suspect height analysis using PhotoModeler","volume":"318","author":"Olver","year":"2021","journal-title":"Forensic Sci. Int."},{"key":"ref_81","unstructured":"Engstr\u00f6m, P. (May, January 27). Visualizations techniques for forensic training applications. Proceedings of the Virtual, Augmented, and Mixed Reality (XR) Technology for Multi-Domain Operations, Online."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"200535","DOI":"10.1016\/j.fri.2023.200535","article-title":"Using the iPhone\u2019s LiDAR technology to capture 3D forensic data at crime and crash scenes","volume":"32","author":"Kottner","year":"2023","journal-title":"Forensic Imaging"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"e2220230","DOI":"10.1590\/2177-6709.27.3.e2220230.oar","article-title":"Evaluation of two stereophotogrametry software for 3D reconstruction of virtual facial models","volume":"27","author":"Chaves","year":"2022","journal-title":"Dent. Press J. Orthod."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"56","DOI":"10.3390\/forensicsci1020008","article-title":"A Study of 3D Digitisation Modalities for Crime Scene Investigation","volume":"1","author":"Galanakis","year":"2021","journal-title":"Forensic Sci."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"111100","DOI":"10.1016\/j.forsciint.2021.111100","article-title":"Laser scanner and drone photogrammetry: A statistical comparison between 3-dimensional models and its impacts on outdoor crime scene registration","volume":"330","author":"Cunha","year":"2022","journal-title":"Forensic Sci. Int."},{"key":"ref_86","unstructured":"Al-Top Topograf\u00eda, S.A. (2025, November 03). Trimble Forensic Reveal. Available online: https:\/\/al-top.com\/producto\/trimble-forensics-reveal\/."},{"key":"ref_87","first-page":"61","article-title":"Forensic architecture: A new dimension in Forensics. Analele \u0218tiin\u021bifice ale Universit\u0103\u0163ii Alexandru Ioan Cuza din Ia\u0219i","volume":"68","year":"2022","journal-title":"Ser. \u015etiin\u0163e Jurid."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Mezhenin, A., Polyakov, V., Prishhepa, A., Izvozchikova, V., and Zykov, A. (2020, January 18\u201320). Using Virtual Scenes for Comparison of Photogrammetry Software. Proceedings of the Advances in Intelligent Systems, Computer Science and Digital Economics II, Moscow, Russia.","DOI":"10.1007\/978-3-030-80478-7_7"},{"key":"ref_89","first-page":"130","article-title":"Area Based Image Matching Methods\u2014A Survey","volume":"2","author":"Joglekar","year":"2012","journal-title":"Int. J. Emerg. Technol. Adv. Eng."},{"key":"ref_90","first-page":"175","article-title":"Adaptive least squares correlation: A powerful image matching technique","volume":"14","author":"Gruen","year":"1985","journal-title":"S. Afr. J. Photogramm. Remote Sens. Cartogr."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1023\/A:1007963824710","article-title":"SUSAN\u2014A New Approach to Low Level Image Processing","volume":"23","author":"Smith","year":"1997","journal-title":"Int. J. Comput. Vis."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1016\/j.imavis.2004.02.006","article-title":"Robust wide-baseline stereo from maximally stable extremal regions","volume":"22","author":"Matas","year":"2004","journal-title":"Image Vis. Comput."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.cviu.2007.09.014","article-title":"Speeded-Up Robust Features (SURF)","volume":"110","author":"Bay","year":"2008","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Dusmanu, M., Rocco, I., Pajdla, T., Pollefeys, M., Sivic, J., Torii, A., and Sattler, T. (2019, January 16\u201320). D2-Net: A Trainable CNN for Joint Description and Detection of Local Features. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00828"},{"key":"ref_95","unstructured":"Revaud, J., De Souza, C., Humenberger, M., and Weinzaepfel, P. (2019, January 8\u201314). R2D2: Reliable and Repeatable Detector and Descriptor. Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, BC, Canada."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive Image Features from Scale-Invariant Keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_97","unstructured":"Wu, C. (2024, October 03). SiftGPU. Available online: https:\/\/github.com\/pitzer\/SiftGPU."},{"key":"ref_98","unstructured":"OpenCV (2024, October 03). SIFT Feature Detection Tutorial. Available online: https:\/\/docs.opencv.org\/4.x\/da\/df5\/tutorial_py_sift_intro.html?ref=blog.roboflow.com."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., and Schmid, C. (2012). KAZE Features. Computer Vision\u2014ECCV 2012, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-642-33712-3"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Alcantarilla, P., Nuevo, J., and Bartoli, A. (2013, January 9\u201313). Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces. Proceedings of the British Machine Vision Conference 2013, Bristol, UK.","DOI":"10.5244\/C.27.13"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011, January 6\u201313). ORB: An efficient alternative to SIFT or SURF. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Leonardis, A., Bischof, H., and Pinz, A. (2006). SURF: Speeded Up Robust Features. Computer Vision\u2014ECCV 2006, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/11744085"},{"key":"ref_103","unstructured":"Muja, M., and Lowe, D.G. (2009, January 5\u20138). Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration. Proceedings of the International Conference on Computer Vision Theory and Applications, Lisboa, Portugal."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"103","DOI":"10.14358\/PERS.81.2.103","article-title":"Direct Linear Transformation from Comparator Coordinates into Object Space Coordinates in Close-Range Photogrammetry","volume":"81","author":"Karara","year":"2015","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1137\/080732730","article-title":"ASIFT: A New Framework for Fully Affine Invariant Image Comparison","volume":"2","author":"Morel","year":"2009","journal-title":"SIAM J. Imaging Sci."},{"key":"ref_107","unstructured":"Walk, S. (2024, September 03). Random Forest Template Forest. Available online: https:\/\/prs.igp.ethz.ch\/research\/Source_code_and_datasets\/legacy-code-and-datasets-archive.html."},{"key":"ref_108","unstructured":"OpenMVS (2024, September 25). OpenMVS\u2014Open Multi-View Stereo Reconstruction Library. Available online: https:\/\/cdcseacave.github.io\/."},{"key":"ref_109","unstructured":"(2024, September 25). ENS Patch-Based Multi-View Stereo Software (PMVS). Available online: https:\/\/www.di.ens.fr\/pmvs\/pmvs-1\/index.html."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Furukawa, Y., and Ponce, J. (2007, January 17\u201322). Accurate, Dense, and Robust Multi-View Stereopsis. Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383246"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Langguth, F., Sunkavalli, K., Hadap, S., and Goesele, M. (2025, November 03). Shading-Aware Multi-view Stereo. In Computer Vision (ECCV). Available online: http:\/\/www.kalyans.org\/research\/2016\/ShadingAwareMVS_ECCV16_supp.pdf.","DOI":"10.1007\/978-3-319-46487-9_29"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Thomas, H., Deschaud, J.-E., Marcotegui, B., Goulette, F., and Gall, Y.L. (2018, January 5\u20138). Semantic Classification of 3D Point Clouds with Multiscale Spherical Neighborhoods. Proceedings of the Internation Conference on 3D Vision (3DV), Verona, Italy.","DOI":"10.1109\/3DV.2018.00052"},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"36","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_117","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016, January 11\u201314). SSD: Single Shot MultiBox Detector. Proceedings of the Computer Vision\u2014ECCV 2016, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal Loss for Dense Object Detection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft COCO: Common Objects in Context. Proceedings of the Computer Vision\u2014ECCV, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Patsiouras, E., Vasileiou, S.K., Papadopoulos, S., Dourvas, N.I., Ioannidis, K., Vrochidis, S., and Kompatsiaris, I. (2024, January 21\u201323). Integrating AI and Computer Vision for Ballistic and Bloodstain Analysis in 3D Digital Forensics. Proceedings of the 2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), St Albans, UK.","DOI":"10.1109\/MetroXRAINE62247.2024.10796512"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/11\/707\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T11:08:45Z","timestamp":1762513725000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/11\/707"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,7]]},"references-count":121,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["a18110707"],"URL":"https:\/\/doi.org\/10.3390\/a18110707","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,7]]}}}