High-fidelity update method for multi-temporal point cloud models with semantic-geometric collaborative constraints

Abstract

Local incremental updating of point cloud models remains a core challenge in urban digital governance, limited by two well-documented bottlenecks: inaccurate semantic boundary localization and pronounced seam artifacts in texture stitching across fused regions. To address these limitations, we propose a high-fidelity multi-temporal point cloud updating method integrating semantic, geometric and texture information under a unified semantic-geometric collaborative constraint framework. The core contribution lies in cross-stage collaborative optimization across semantic boundary extraction, point cloud registration and texture fusion. Such improvements stem not from breakthroughs in individual sub-algorithms, but from the integrated pipeline that suppresses cumulative error propagation and delivers steady performance improvements across all processing stages. Specifically, we adapt the classic Cloth Simulation Filtering (CSF) into a semantic-adaptive variant (SA-CSF) as the upstream module, adjusting mechanical constraints with adaptive semantic weighting to distinguish true structural boundaries from pseudo-edges caused by dynamic interference. Built upon these boundary outputs, a spatial topology-aware BiResNet pipeline integrates established position-aware convolution and Graph Neural Network architectures to reconstruct topology in occluded regions and mitigate over-reliance on precise initial poses. For texture harmonization, gradient-domain fusion frameworks are extended into a semantic-weighted multi-scale Poisson fusion mechanism with an enhanced Phong model, achieving joint optimization of illumination correction and fine-grained texture retention. Evaluated on point cloud data from Chuzhou University’s Huifeng Campus, the method achieves 92.5% average class accuracy and 2.1 cm 95% Hausdorff distance (95% HD) for semantic boundary extraction, 91.6% registration success rate with 0.21 m Chamfer distance, and 90.10% texture detail preservation rate with 51.24% texture seam visibility. While not top-ranked on either texture metric, the method achieves the optimal overall trade-off between seam visibility reduction and detail preservation. This balance holds notable practical value for engineering applications: in mixed urban scenarios, single-metric methods (e.g., Graph Cut for detail, Mean Value Coordinates for seams) either compromise overall visual coherence or lose high-frequency details critical for fine-grained management. The unified pipeline also reduces scene-specific parameter tuning effort, lowering operational costs for batch projects. This work provides an effective solution for high-fidelity multi-temporal point cloud updating and supports digital twin city construction and urban digital governance.