are rapidly becoming essential infrastructure for modern buildings, enabling indoor navigation, facility management, safety planning, and operational analytics. However, the process of creating digital twins still relies heavily on manual reconstruction of buildings from LiDAR scans.<\/span><\/p>\nA geospatial technology company approached our team to solve this bottleneck. Their workflow relied on mobile LiDAR scanning systems that produced dense point clouds of indoor environments, containing millions of spatial measurements. Before these scans could be used in their digital twin platform, the data had to be converted into structured BIM models.<\/span><\/p>\nThis conversion required specialists to manually reconstruct architectural elements, including walls, doors, windows, floors, and structural components. For large buildings, the process could take days or even weeks, limiting the scalability of the client\u2019s operations.<\/span><\/p>\nOur goal was to design a machine learning system capable of automatically converting point clouds into BIM-ready architectural structures. The client did not require full automation; a solution that could automate up to 70\u201380%<\/strong> of building element extraction would already deliver significant operational benefits.<\/p>\nThe resulting solution combines deep learning-based 3D scene understanding with geometric reconstruction algorithms, enabling automated generation of structured BIM models directly from LiDAR scans.<\/span><\/p>\n[\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_text_color=”#13151d” text_line_height=”1.6em” header_2_font=”–et_global_body_font|500|||||||” header_2_font_size=”36px” header_2_line_height=”1.5em” width_tablet=”65%” width_phone=”100%” width_last_edited=”on|desktop” max_width=”800px” module_alignment=”center” custom_margin=”|0px|||false|false” custom_margin_tablet=”|0px|48px||false|false” custom_margin_phone=”|0px|32px||false|false” custom_margin_last_edited=”on|desktop” custom_padding=”|0px||0px|false|false” text_font_size_tablet=”” text_font_size_phone=”16px” text_font_size_last_edited=”on|desktop” header_2_font_size_phone=”15px” module_alignment_last_edited=”off|desktop” global_colors_info=”{}”]<\/p>\n
<\/span>The Challenge<\/b><\/span><\/h2>\n[\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_text_color=”#13151d” text_line_height=”1.6em” link_font=”–et_global_body_font||||on|||#13151d|” link_text_color=”#13151d” header_2_font=”Work Sans|700|||||||” header_2_font_size=”36px” header_2_line_height=”1.5em” width_tablet=”65%” width_phone=”100%” width_last_edited=”on|desktop” max_width=”800px” module_alignment=”center” custom_margin=”|0px|48px||false|false” custom_margin_tablet=”|0px|48px||false|false” custom_margin_phone=”|0px|32px||false|false” custom_margin_last_edited=”on|desktop” custom_padding=”|0px||0px|false|false” text_font_size_tablet=”” text_font_size_phone=”16px” text_font_size_last_edited=”on|desktop” header_2_font_size_phone=”15px” module_alignment_last_edited=”off|desktop” global_colors_info=”{}”]<\/p>\n
LiDAR scanners capture buildings with extraordinary precision, but the resulting point clouds are inherently unstructured. A typical indoor scan may contain tens or hundreds of millions of points representing surfaces throughout the building.<\/span><\/p>\nAlthough this data accurately represents the physical environment, it lacks semantic meaning. A point cloud does not explicitly describe which points represent walls, doors, columns, or mechanical systems. Human engineers must interpret this structure and manually recreate the building inside BIM software.<\/span><\/p>\nThis manual step represents one of the largest bottlenecks in the production of digital twins. The challenge was therefore not simply processing large volumes of 3D data, but teaching machines to understand architectural structure within that data.<\/span><\/p>\n[\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_text_color=”#13151d” text_line_height=”1.6em” header_2_font=”–et_global_body_font||||||||” header_2_font_size=”36px” header_2_line_height=”1.5em” width_tablet=”65%” width_phone=”100%” width_last_edited=”on|desktop” max_width=”800px” module_alignment=”center” custom_margin=”|0px|||false|false” custom_margin_tablet=”|0px|48px||false|false” custom_margin_phone=”|0px|32px||false|false” custom_margin_last_edited=”on|desktop” custom_padding=”|0px||0px|false|false” text_font_size_tablet=”” text_font_size_phone=”16px” text_font_size_last_edited=”on|desktop” header_2_font_size_phone=”15px” module_alignment_last_edited=”off|desktop” global_colors_info=”{}”]<\/p>\n
<\/span>Solution Architecture: How LiDAR Point Clouds are Converted into BIM<\/strong><\/span><\/h2>\n[\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_text_color=”#13151d” text_line_height=”1.6em” link_font=”–et_global_body_font||||on|||#13151d|” link_text_color=”#13151d” header_2_font=”–et_global_body_font||||||||” header_2_font_size=”36px” header_2_line_height=”1.5em” header_3_font=”–et_global_body_font|600|||||||” width_tablet=”65%” width_phone=”100%” width_last_edited=”on|desktop” max_width=”800px” module_alignment=”center” custom_margin=”|0px|48px||false|false” custom_margin_tablet=”|0px|48px||false|false” custom_margin_phone=”|0px|32px||false|false” custom_margin_last_edited=”on|desktop” custom_padding=”|0px||0px|false|false” text_font_size_tablet=”” text_font_size_phone=”16px” text_font_size_last_edited=”on|desktop” header_2_font_size_phone=”15px” module_alignment_last_edited=”off|desktop” global_colors_info=”{}”]<\/p>\n
The system we designed follows a multi-stage pipeline that converts raw spatial data into structured building models.<\/span><\/p>\n[\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_text_color=”#13151d” text_line_height=”1.6em” header_2_font=”–et_global_body_font||||||||” header_2_font_size=”36px” header_2_line_height=”1.5em” header_3_font=”–et_global_body_font|500|||||||” width_tablet=”65%” width_phone=”100%” width_last_edited=”on|desktop” max_width=”800px” module_alignment=”center” custom_margin=”|0px|||false|false” custom_margin_tablet=”|0px|48px||false|false” custom_margin_phone=”|0px|32px||false|false” custom_margin_last_edited=”on|desktop” custom_padding=”|0px||0px|false|false” text_font_size_tablet=”” text_font_size_phone=”16px” text_font_size_last_edited=”on|desktop” header_2_font_size_phone=”15px” module_alignment_last_edited=”off|desktop” global_colors_info=”{}”]<\/p>\n
<\/span>Stage 1: Point Cloud Preprocessing<\/span><\/h3>\n[\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_text_color=”#13151d” text_line_height=”1.6em” link_font=”–et_global_body_font||||on|||#13151d|” link_text_color=”#13151d” header_2_font=”–et_global_body_font||||||||” header_2_font_size=”36px” header_2_line_height=”1.5em” header_3_font=”–et_global_body_font|600|||||||” width_tablet=”65%” width_phone=”100%” width_last_edited=”on|desktop” max_width=”800px” module_alignment=”center” custom_margin=”|0px|48px||false|false” custom_margin_tablet=”|0px|48px||false|false” custom_margin_phone=”|0px|32px||false|false” custom_margin_last_edited=”on|desktop” custom_padding=”|0px||0px|false|false” text_font_size_tablet=”” text_font_size_phone=”16px” text_font_size_last_edited=”on|desktop” header_2_font_size_phone=”15px” module_alignment_last_edited=”off|desktop” global_colors_info=”{}”]<\/p>\n
The incoming point cloud is first standardized to ensure consistent processing by machine learning models. This stage includes coordinate normalization, noise filtering, density balancing, and spatial partitioning of large point clouds into manageable segments suitable for GPU processing.<\/span><\/p>\nPartitioning is particularly important because indoor scans often contain hundreds of millions of points, requiring efficient memory management during model inference.<\/span><\/p>\nThis stage was implemented using Python-based data processing pipelines, leveraging OpenCV<\/strong> for preprocessing utilities and PCL (Point Cloud Library)<\/strong> for point cloud filtering, geometric transformations, and spatial data manipulation.<\/span><\/p>\n[\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_text_color=”#13151d” text_line_height=”1.6em” header_2_font=”–et_global_body_font||||||||” header_2_font_size=”36px” header_2_line_height=”1.5em” header_3_font=”–et_global_body_font|500|||||||” width_tablet=”65%” width_phone=”100%” width_last_edited=”on|desktop” max_width=”800px” module_alignment=”center” custom_margin=”|0px|||false|false” custom_margin_tablet=”|0px|48px||false|false” custom_margin_phone=”|0px|32px||false|false” custom_margin_last_edited=”on|desktop” custom_padding=”|0px||0px|false|false” text_font_size_tablet=”” text_font_size_phone=”16px” text_font_size_last_edited=”on|desktop” header_2_font_size_phone=”15px” module_alignment_last_edited=”off|desktop” global_colors_info=”{}”]<\/p>\n
<\/span>Stage 2: Semantic Segmentation for Point Cloud to BIM Conversion<\/span><\/h3>\n[\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_text_color=”#13151d” text_line_height=”1.6em” link_font=”–et_global_body_font||||on|||#13151d|” link_text_color=”#13151d” header_2_font=”–et_global_body_font||||||||” header_2_font_size=”36px” header_2_line_height=”1.5em” header_3_font=”–et_global_body_font|600|||||||” width_tablet=”65%” width_phone=”100%” width_last_edited=”on|desktop” max_width=”800px” module_alignment=”center” custom_margin=”|0px|48px||false|false” custom_margin_tablet=”|0px|48px||false|false” custom_margin_phone=”|0px|32px||false|false” custom_margin_last_edited=”on|desktop” custom_padding=”|0px||0px|false|false” hover_enabled=”0″ text_font_size_tablet=”” text_font_size_phone=”16px” text_font_size_last_edited=”on|desktop” header_2_font_size_phone=”15px” module_alignment_last_edited=”off|desktop” global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n
The core of the system is a deep learning model designed for 3D semantic segmentation. The model analyzes the spatial structure of the point cloud and classifies each point according to its architectural role.<\/span><\/p>\nTypical classes include:<\/span><\/p>\n– walls<\/span>
<\/span>– floors<\/span>
<\/span>– roofs<\/span>
<\/span>– doors<\/span>
<\/span>– windows<\/span>
<\/span>– structural columns<\/span>
<\/span>– pipes and mechanical elements<\/span>
<\/span>– equipment and furniture<\/span><\/p>\nTo determine the most suitable architecture, multiple state-of-the-art 3D models were evaluated during the research phase. Candidate models included transformer-based architectures and sparse convolutional neural networks commonly used for large-scale 3D scene understanding.<\/span><\/p>\nThe implementation was built using Python-based deep learning frameworks, including PyTorch<\/strong>, enabling flexible experimentation with different model architectures.<\/span><\/p>\nFor large-scale point cloud understanding, the team leveraged the Pointcept framework, an open-source platform for advanced 3D scene understanding and point cloud segmentation. Pointcept provides implementations of modern transformer-based architectures optimized for large spatial datasets.<\/span><\/p>\nPoint Transformer V2 <\/strong>point cloud architectures demonstrated the strongest performance due to their ability to capture long-range spatial relationships across complex indoor environments. These models learn architectural patterns directly from data, enabling them to recognize structural components even in noisy, irregular point clouds.<\/span><\/p>\n[\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_text_color=”#13151d” text_line_height=”1.6em” header_2_font=”–et_global_body_font||||||||” header_2_font_size=”36px” header_2_line_height=”1.5em” header_3_font=”–et_global_body_font|500|||||||” width_tablet=”65%” width_phone=”100%” width_last_edited=”on|desktop” max_width=”800px” module_alignment=”center” custom_margin=”|0px|||false|false” custom_margin_tablet=”|0px|48px||false|false” custom_margin_phone=”|0px|32px||false|false” custom_margin_last_edited=”on|desktop” custom_padding=”|0px||0px|false|false” text_font_size_tablet=”” text_font_size_phone=”16px” text_font_size_last_edited=”on|desktop” header_2_font_size_phone=”15px” module_alignment_last_edited=”off|desktop” global_colors_info=”{}”]<\/p>\n
<\/span>Stage 3: Geometry Reconstruction for BIM-Ready Models<\/span><\/h3>\n[\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_text_color=”#13151d” text_line_height=”1.6em” link_font=”–et_global_body_font||||on|||#13151d|” link_text_color=”#13151d” header_2_font=”–et_global_body_font||||||||” header_2_font_size=”36px” header_2_line_height=”1.5em” header_3_font=”–et_global_body_font|600|||||||” width_tablet=”65%” width_phone=”100%” width_last_edited=”on|desktop” max_width=”800px” module_alignment=”center” custom_margin=”|0px|48px||false|false” custom_margin_tablet=”|0px|48px||false|false” custom_margin_phone=”|0px|32px||false|false” custom_margin_last_edited=”on|desktop” custom_padding=”|0px||0px|false|false” text_font_size_tablet=”” text_font_size_phone=”16px” text_font_size_last_edited=”on|desktop” header_2_font_size_phone=”15px” module_alignment_last_edited=”off|desktop” global_colors_info=”{}”]<\/p>\n
Once the building’s semantic structure is identified, the system converts labeled point clusters into architectural geometry.<\/span><\/p>\nRaw point clouds are inherently noisy and irregular, while BIM environments require precise parametric elements. To bridge this gap, we implemented a geometry reconstruction layer that applies geometric fitting algorithms and architectural constraints.<\/span><\/p>\nExamples include:<\/span><\/p>\n– plane fitting for walls, floors, and ceilings<\/span>
<\/span>– detection of openings within wall segments for doors and windows<\/span>
<\/span>– primitive fitting for columns and vertical structures<\/span>
<\/span>– orthogonalization of intersecting surfaces<\/span><\/p>\nThese algorithms transform segmented point clusters into clean architectural primitives suitable for BIM modeling<\/strong>.<\/span><\/p>\nThis stage relies heavily on the Point Cloud Library (PCL)<\/strong> for geometric operations such as plane extraction, clustering, and primitive fitting.<\/span><\/p>\nAdditional post-processing ensures that the resulting structures adhere to architectural conventions, such as right-angle intersections between walls and consistent wall thickness.<\/span><\/p>\n[\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_text_color=”#13151d” text_line_height=”1.6em” header_2_font=”–et_global_body_font||||||||” header_2_font_size=”36px” header_2_line_height=”1.5em” header_3_font=”–et_global_body_font|500|||||||” width_tablet=”65%” width_phone=”100%” width_last_edited=”on|desktop” max_width=”800px” module_alignment=”center” custom_margin=”|0px|||false|false” custom_margin_tablet=”|0px|48px||false|false” custom_margin_phone=”|0px|32px||false|false” custom_margin_last_edited=”on|desktop” custom_padding=”|0px||0px|false|false” text_font_size_tablet=”” text_font_size_phone=”16px” text_font_size_last_edited=”on|desktop” header_2_font_size_phone=”15px” module_alignment_last_edited=”off|desktop” global_colors_info=”{}”]<\/p>\n
<\/span>Stage 4: Automating Scan-to-BIM for Digital Twin Creation<\/span><\/h3>\n[\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_text_color=”#13151d” text_line_height=”1.6em” link_font=”–et_global_body_font||||on|||#13151d|” link_text_color=”#13151d” header_2_font=”–et_global_body_font||||||||” header_2_font_size=”36px” header_2_line_height=”1.5em” header_3_font=”–et_global_body_font|600|||||||” width_tablet=”65%” width_phone=”100%” width_last_edited=”on|desktop” max_width=”800px” module_alignment=”center” custom_margin=”|0px|48px||false|false” custom_margin_tablet=”|0px|48px||false|false” custom_margin_phone=”|0px|32px||false|false” custom_margin_last_edited=”on|desktop” custom_padding=”|0px||0px|false|false” text_font_size_tablet=”” text_font_size_phone=”16px” text_font_size_last_edited=”on|desktop” header_2_font_size_phone=”15px” module_alignment_last_edited=”off|desktop” global_colors_info=”{}”]<\/p>\n
The reconstructed geometry is converted into parametric BIM elements and exported in industry-standard formats compatible with digital twin platforms.<\/span><\/p>\nThe system integrates with industry-standard BIM and CAD tools, including:<\/span><\/p>\n– AutoCAD
– Autodesk Revit
– BIM 360<\/strong><\/p>\nThese tools are used to generate structured architectural objects and manage BIM datasets within collaborative design environments.<\/span><\/p>\nThe generated models include structured building components such as walls, floors, doors, windows, and structural columns. Because the geometry is already organized into BIM primitives, it can be directly integrated into spatial analytics and indoor mapping workflows.<\/span><\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/timspark.com\/wp-content\/uploads\/2026\/03\/Solution-Architecture-scaled.webp” alt=”Iceberg-style infographic about mobile app budgeting across the full lifecycle, showing hidden long-term costs such as security patches, OS updates, compliance changes, and UX iteration.” title_text=”Solution Architecture” show_in_lightbox=”on” disabled_on=”off|off|off” admin_label=”Image” _builder_version=”4.27.4″ _module_preset=”default” width=”80%” width_tablet=”80%” width_phone=”100%” width_last_edited=”on|phone” max_width=”1080px” module_alignment=”center” custom_margin=”||48px||false|false” custom_margin_tablet=”||64px||false|false” custom_margin_phone=”||64px||false|false” custom_margin_last_edited=”on|desktop” border_radii=”on|12px|12px|12px|12px” border_width_all=”1px” border_color_all=”#eaeaea” locked=”off” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_text_color=”#13151d” text_line_height=”1.6em” header_2_font=”–et_global_body_font||||||||” header_2_font_size=”36px” header_2_line_height=”1.5em” header_3_font=”–et_global_body_font|500|||||||” width_tablet=”65%” width_phone=”100%” width_last_edited=”on|desktop” max_width=”800px” module_alignment=”center” custom_margin=”|0px|||false|false” custom_margin_tablet=”|0px|48px||false|false” custom_margin_phone=”|0px|32px||false|false” custom_margin_last_edited=”on|desktop” custom_padding=”|0px||0px|false|false” text_font_size_tablet=”” text_font_size_phone=”16px” text_font_size_last_edited=”on|desktop” header_2_font_size_phone=”15px” module_alignment_last_edited=”off|desktop” global_colors_info=”{}”]<\/p>\n
<\/span>Machine Learning Architecture<\/b><\/span><\/h3>\n[\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_text_color=”#13151d” text_line_height=”1.6em” link_font=”–et_global_body_font||||on|||#13151d|” link_text_color=”#13151d” header_2_font=”Work Sans|700|||||||” header_2_font_size=”36px” header_2_line_height=”1.5em” width_tablet=”65%” width_phone=”100%” width_last_edited=”on|desktop” max_width=”800px” module_alignment=”center” custom_margin=”|0px|48px||false|false” custom_margin_tablet=”|0px|48px||false|false” custom_margin_phone=”|0px|32px||false|false” custom_margin_last_edited=”on|desktop” custom_padding=”|0px||0px|false|false” text_font_size_tablet=”” text_font_size_phone=”16px” text_font_size_last_edited=”on|desktop” header_2_font_size_phone=”15px” module_alignment_last_edited=”off|desktop” global_colors_info=”{}”]<\/p>\n
During the research phase, we evaluated multiple model families using a standard benchmarking pipeline:<\/span><\/p>\nData \u2013> Model Candidate \u2013> <\/span>Performance Metrics<\/b><\/p>\nThe deep learning training pipeline was implemented using <\/span>PyTorch and TensorFlow<\/b>, enabling distributed model training on GPU clusters.<\/span><\/p>\n[\/et_pb_text][et_pb_image src=”https:\/\/timspark.com\/wp-content\/uploads\/2026\/03\/Machine-Learning-Architecture-scaled.webp” alt=”Iceberg-style infographic about mobile app budgeting across the full lifecycle, showing hidden long-term costs such as security patches, OS updates, compliance changes, and UX iteration.” title_text=”Machine Learning Architecture” show_in_lightbox=”on” disabled_on=”off|off|off” admin_label=”Image” _builder_version=”4.27.4″ _module_preset=”default” width=”80%” width_tablet=”80%” width_phone=”100%” width_last_edited=”on|phone” max_width=”1080px” module_alignment=”center” custom_margin=”||48px||false|false” custom_margin_tablet=”||64px||false|false” custom_margin_phone=”||64px||false|false” custom_margin_last_edited=”on|desktop” border_radii=”on|12px|12px|12px|12px” border_width_all=”1px” border_color_all=”#eaeaea” locked=”off” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font=”Fira Sans|500|||||||” text_text_color=”#13151d” text_line_height=”1.6em” header_2_font=”Work Sans|700|||||||” header_2_font_size=”36px” header_2_line_height=”1.5em” header_3_font=”–et_global_body_font||||||||” width_tablet=”65%” width_phone=”100%” width_last_edited=”on|desktop” max_width=”800px” module_alignment=”center” custom_margin=”|0px|||false|false” custom_margin_tablet=”|0px|48px||false|false” custom_margin_phone=”|0px|32px||false|false” custom_margin_last_edited=”on|desktop” custom_padding=”|0px||0px|false|false” text_font_size_tablet=”” text_font_size_phone=”16px” text_font_size_last_edited=”on|desktop” header_2_font_size_phone=”15px” module_alignment_last_edited=”off|desktop” global_colors_info=”{}”]<\/p>\n
<\/span>Model Performance<\/strong><\/span><\/h3>\n[\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_text_color=”#13151d” text_line_height=”1.6em” link_font=”–et_global_body_font||||on|||#13151d|” link_text_color=”#13151d” header_2_font=”Work Sans|700|||||||” header_2_font_size=”36px” header_2_line_height=”1.5em” width_tablet=”65%” width_phone=”100%” width_last_edited=”on|desktop” max_width=”800px” module_alignment=”center” custom_margin=”|0px|48px||false|false” custom_margin_tablet=”|0px|48px||false|false” custom_margin_phone=”|0px|32px||false|false” custom_margin_last_edited=”on|desktop” custom_padding=”|0px||0px|false|false” hover_enabled=”0″ text_font_size_tablet=”” text_font_size_phone=”16px” text_font_size_last_edited=”on|desktop” header_2_font_size_phone=”15px” module_alignment_last_edited=”off|desktop” global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n
The final trained model achieved strong overall performance, maintaining high reliability for automated BIM generation from LiDAR point clouds<\/strong>, with lower accuracy observed mainly in smaller or more complex elements such as doors.<\/p>\nOverall performance metrics included:<\/span><\/p>\n– Mean Intersection-over-Union (mIoU): 0.7631<\/span>
<\/span>– Mean classification accuracy (mAcc): 0.8827<\/span><\/p>\nPer-class performance highlights include:<\/span><\/p>\n– Floors: IoU 0.9448<\/span>
<\/span>– Walls: IoU 0.8167<\/span>
<\/span>– Windows: IoU 0.8311<\/span>
<\/span>– Doors: IoU 0.6445<\/span>
<\/span>– Structural Columns: IoU 0.6989<\/span><\/p>\nThese results met the project objective of 70\u201380% automated BIM element extraction, with the remainder handled through manual verification and refinement.<\/span><\/p>\n[\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font=”Fira Sans|500|||||||” text_text_color=”#13151d” text_line_height=”1.6em” header_2_font=”Work Sans|700|||||||” header_2_font_size=”36px” header_2_line_height=”1.5em” header_3_font=”–et_global_body_font||||||||” width_tablet=”65%” width_phone=”100%” width_last_edited=”on|desktop” max_width=”800px” module_alignment=”center” custom_margin=”|0px|||false|false” custom_margin_tablet=”|0px|48px||false|false” custom_margin_phone=”|0px|32px||false|false” custom_margin_last_edited=”on|desktop” custom_padding=”|0px||0px|false|false” text_font_size_tablet=”” text_font_size_phone=”16px” text_font_size_last_edited=”on|desktop” header_2_font_size_phone=”15px” module_alignment_last_edited=”off|desktop” global_colors_info=”{}”]<\/p>\n
<\/span>Deployment Architecture<\/strong><\/span><\/h3>\n[\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_text_color=”#13151d” text_line_height=”1.6em” link_font=”–et_global_body_font||||on|||#13151d|” link_text_color=”#13151d” header_2_font=”Work Sans|700|||||||” header_2_font_size=”36px” header_2_line_height=”1.5em” width_tablet=”65%” width_phone=”100%” width_last_edited=”on|desktop” max_width=”800px” module_alignment=”center” custom_margin=”|0px|48px||false|false” custom_margin_tablet=”|0px|48px||false|false” custom_margin_phone=”|0px|32px||false|false” custom_margin_last_edited=”on|desktop” custom_padding=”|0px||0px|false|false” text_font_size_tablet=”” text_font_size_phone=”16px” text_font_size_last_edited=”on|desktop” header_2_font_size_phone=”15px” module_alignment_last_edited=”off|desktop” global_colors_info=”{}”]<\/p>\n
The solution was deployed as a scalable machine learning pipeline running on cloud infrastructure in AWS and Azure. These environments provided the GPU resources required for model training and inference on large point cloud datasets.<\/span><\/p>\nThe production system includes:<\/span><\/p>\n– Python-based ML inference services<\/span>
<\/span>– deep learning models trained with TensorFlow and PyTorch<\/span>
<\/span>– point cloud processing modules built with PCL<\/span>
<\/span>– geometry reconstruction pipelines<\/span>
<\/span>– BIM export modules integrated with AutoCAD, Revit, and BIM 360<\/span><\/p>\nThis modular architecture allows the client to automatically process new scans as they are generated, transforming raw spatial data into structured digital building models.<\/span><\/p>\n[\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font=”Fira Sans|500|||||||” text_text_color=”#13151d” text_line_height=”1.6em” header_2_font=”–et_global_body_font||||||||” header_2_font_size=”36px” header_2_line_height=”1.5em” header_3_font=”Anek Latin|500|||||||” width_tablet=”65%” width_phone=”100%” width_last_edited=”on|desktop” max_width=”800px” module_alignment=”center” custom_margin=”|0px|||false|false” custom_margin_tablet=”|0px|48px||false|false” custom_margin_phone=”|0px|32px||false|false” custom_margin_last_edited=”on|desktop” custom_padding=”|0px||0px|false|false” text_font_size_tablet=”” text_font_size_phone=”16px” text_font_size_last_edited=”on|desktop” header_2_font_size_phone=”15px” module_alignment_last_edited=”off|desktop” global_colors_info=”{}”]<\/p>\n
<\/span>Results & Impact<\/strong><\/span><\/h2>\n[\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_text_color=”#13151d” text_line_height=”1.6em” link_font=”–et_global_body_font||||on|||#13151d|” link_text_color=”#13151d” header_2_font=”Work Sans|700|||||||” header_2_font_size=”36px” header_2_line_height=”1.5em” width_tablet=”65%” width_phone=”100%” width_last_edited=”on|desktop” max_width=”800px” module_alignment=”center” custom_margin=”|0px|48px||false|false” custom_margin_tablet=”|0px|48px||false|false” custom_margin_phone=”|0px|32px||false|false” custom_margin_last_edited=”on|desktop” custom_padding=”|0px||0px|false|false” text_font_size_tablet=”” text_font_size_phone=”16px” text_font_size_last_edited=”on|desktop” header_2_font_size_phone=”15px” module_alignment_last_edited=”off|desktop” global_colors_info=”{}”]<\/p>\n
The implementation significantly improved the efficiency of the digital twin production pipeline.<\/span><\/p>\n