<p>Construction digital twins aim to maintain an up-to-date, BIM-consistent as-built state from streaming multi-line LiDAR to enable progress verification, dimensional compliance control, and proactive safety management on construction sites. However, converting sequential LiDAR observations into a temporally stable, element-centric BIM state is difficult in realistic AEC environments. Existing approaches mainly fall into geometry-driven Scan-to-BIM registration frameworks and learning-based point cloud perception models, with some system-level digital-twin frameworks built on BIM-centric reasoning. Purely geometric alignment is brittle under occlusion, clutter, and repetitive structures, and registration uncertainty can propagate to downstream progress and deviation conclusions. Meanwhile, generic perception networks are often trained on static frames, lack temporal stabilization, and do not explicitly optimize BIM-consistent element association or geometry-aware alignment cues. In addition, many digital-twin systems expose limited, loosely coupled state representations that hinder causal, reliable site updates. To address these gaps, we propose LiBiDT (LiDAR–BIM Digital Twin), a causal LiDAR–BIM digital-twin model—where “causal” refers to computational online updating in which only past observations enter the current state—that integrates spatio-temporal mapping, geometric–semantic perception, and scan-to-BIM association for consistent state evolution. LiBiDT consists of Spatio-Temporal Global Registration (STGR) with LiDAR–IMU–GNSS priors, Multi-task Perception (MTP) using a PointNet++ backbone with semantic/instance/normal heads, and Scan-to-BIM Association and State Updating (S2B) via normal-guided point-to-plane refinement, bipartite matching with fused overlap/deviation/semantic evidence, and exponential moving average (EMA) stabilization. The Decision and Risk Analytics (DRA) layer turns the BIM-aligned state into auditable construction-management indicators—weighted completion, deviation summaries, and three forward-evaluable safety/compliance hazard flags. Experiments on three public benchmarks (Hilti SLAM, CV4AEC 2024 Scan-to-BIM, and S3DIS Area-5) cover trajectory, perception, and association evaluation. On CV4AEC 2024 Scan-to-BIM, LiBiDT reaches association F1 = 0.837 and Chamfer Distance (CD) / surface Root Mean Square Error (RMSE) of 3.2/3.5 cm; relative to the perception-replacement baseline (PointNet++ + S2B, F1 = 0.758) the gain is large, while relative to the strongest competing baseline—CoFiNet (Reg) + S2B at F1 = 0.822—the absolute association gain narrows to 0.015 F1 points, with the bigger gains coming from temporal identity consistency (IDF1 0.854 <InlineEquation ID="IEq1"><EquationSource Format="TEX">\(\rightarrow\)</EquationSource></InlineEquation> 0.861) and ID-switch reduction. A held-out tile-stream case study visualises BIM-element state evolution, alignment refinement, EMA-stabilised evidence streams, and hazard-flag firing as the scan progressively covers the floor; a strict temporal validation under active construction will require time-stamped multi-acquisition data and is left to future work. Ablation results confirm that evidence stabilization and multi-cue matching contribute complementary gains in accuracy and temporal consistency, yielding a favorable efficiency–accuracy trade-off.</p>

Multi-line LiDAR 3D construction environment modeling and BIM consistency update method for digital twins

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2026年6月15日

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Chen Liu,
Xin Xu,
Xiaofeng Ding,
Guangmei He

摘要

Construction digital twins aim to maintain an up-to-date, BIM-consistent as-built state from streaming multi-line LiDAR to enable progress verification, dimensional compliance control, and proactive safety management on construction sites. However, converting sequential LiDAR observations into a temporally stable, element-centric BIM state is difficult in realistic AEC environments. Existing approaches mainly fall into geometry-driven Scan-to-BIM registration frameworks and learning-based point cloud perception models, with some system-level digital-twin frameworks built on BIM-centric reasoning. Purely geometric alignment is brittle under occlusion, clutter, and repetitive structures, and registration uncertainty can propagate to downstream progress and deviation conclusions. Meanwhile, generic perception networks are often trained on static frames, lack temporal stabilization, and do not explicitly optimize BIM-consistent element association or geometry-aware alignment cues. In addition, many digital-twin systems expose limited, loosely coupled state representations that hinder causal, reliable site updates. To address these gaps, we propose LiBiDT (LiDAR–BIM Digital Twin), a causal LiDAR–BIM digital-twin model—where “causal” refers to computational online updating in which only past observations enter the current state—that integrates spatio-temporal mapping, geometric–semantic perception, and scan-to-BIM association for consistent state evolution. LiBiDT consists of Spatio-Temporal Global Registration (STGR) with LiDAR–IMU–GNSS priors, Multi-task Perception (MTP) using a PointNet++ backbone with semantic/instance/normal heads, and Scan-to-BIM Association and State Updating (S2B) via normal-guided point-to-plane refinement, bipartite matching with fused overlap/deviation/semantic evidence, and exponential moving average (EMA) stabilization. The Decision and Risk Analytics (DRA) layer turns the BIM-aligned state into auditable construction-management indicators—weighted completion, deviation summaries, and three forward-evaluable safety/compliance hazard flags. Experiments on three public benchmarks (Hilti SLAM, CV4AEC 2024 Scan-to-BIM, and S3DIS Area-5) cover trajectory, perception, and association evaluation. On CV4AEC 2024 Scan-to-BIM, LiBiDT reaches association F1 = 0.837 and Chamfer Distance (CD) / surface Root Mean Square Error (RMSE) of 3.2/3.5 cm; relative to the perception-replacement baseline (PointNet++ + S2B, F1 = 0.758) the gain is large, while relative to the strongest competing baseline—CoFiNet (Reg) + S2B at F1 = 0.822—the absolute association gain narrows to 0.015 F1 points, with the bigger gains coming from temporal identity consistency (IDF1 0.854 \(\rightarrow\) 0.861) and ID-switch reduction. A held-out tile-stream case study visualises BIM-element state evolution, alignment refinement, EMA-stabilised evidence streams, and hazard-flag firing as the scan progressively covers the floor; a strict temporal validation under active construction will require time-stamped multi-acquisition data and is left to future work. Ablation results confirm that evidence stabilization and multi-cue matching contribute complementary gains in accuracy and temporal consistency, yielding a favorable efficiency–accuracy trade-off.