Airborne Point Cloud Fusion with Local Plane Constraints for Advanced Semantic Consistency.
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| Title: | Airborne Point Cloud Fusion with Local Plane Constraints for Advanced Semantic Consistency. |
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| Authors: | Parvaz, Shahoriar1 (AUTHOR) shahoriar.parvaz@uni.lu, Teferle, Felicia N.1,2 (AUTHOR), Nurunnabi, Abdul1,3 (AUTHOR), Lindenbergh, Roderik1,2 (AUTHOR), Leiva, Luis A.2,3 (AUTHOR) |
| Source: | Remote Sensing. Aug2026, Vol. 18 Issue 15, p2598. 21p. |
| Subjects: | Point cloud, Multisensor data fusion, Geospatial data, Three-dimensional imaging |
| Abstract: | Highlights: This study presents an airborne cross-source point cloud fusion framework that leverages local plane constraints and point-to-plane alignment to improve geometric consistency between LiDAR and photogrammetric data, particularly Dense Image Matching (DIM) point clouds. Unlike conventional workflows for cross-source fusion, which end with rigid registration or georeferencing datasets into a common reference frame, this research goes further by extracting PCA-derived local planar features and incorporating them into the fusion process to enhance surface preservation and reduce structural inconsistencies arising from density variations, noise, and acquisition differences. Experimental results demonstrate improved semantic consistency and a more reliable representation of urban features, particularly for buildings and ground surfaces, compared with conventional fusion strategies. What are the main findings? The proposed point-to-plane sequential refinement for the cross-source fusion strategy significantly improved geometric alignment and semantic consistency between airborne LiDAR and DIM point clouds in complex urban environments. Incorporating PCA-based local plane constraints improved surface preservation and reduced registration errors caused by differences in sensor accuracy, noise, and other cross-source discrepancies. What are the implications of the main findings? Improved fusion quality can support more accurate automated 3D city modeling, urban mapping, and semantic segmentation applications using heterogeneous airborne datasets. The proposed framework demonstrates that the local plane constraint in airborne cross-source point cloud fusion provides a robust alternative to conventional fusion methods for large-scale dataset integration and reconstruction. Point cloud fusion is crucial in geospatial analysis, combining data from multiple sources (e.g, LiDAR and photogrammetry) to provide a more complete and accurate environmental representation. However, integrating airborne hybrid sensors or cross-source point clouds remains challenging due to variations in geometric accuracy, data precision, gaps, and sensor attributes. Despite recent advancements, these challenges remain and are among the most demanding aspects in geospatial data processing for remote sensing applications. We propose a new point cloud fusion algorithm that leverages local plane constraints to achieve advanced semantic consistency. The proposed method dynamically fits local planes to the target point clouds, enabling robust alignment of source points to these planes. Evaluation on two real-world datasets demonstrates significant gains in accuracy and preservation of geometric details. Our algorithm also improves the accuracy of downstream tasks such as semantic segmentation. In our experiment, the overall accuracy for the Dudelange dataset increases from 48.5% to 80.1%, and that for the Dublin dataset increases from 72.9% to 88.0%. While challenges persist with sparse and noisy datasets, experimental results highlight the effectiveness of the proposed method, offering valuable insights for maximizing the potential of cross-source point cloud data. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: This study presents an airborne cross-source point cloud fusion framework that leverages local plane constraints and point-to-plane alignment to improve geometric consistency between LiDAR and photogrammetric data, particularly Dense Image Matching (DIM) point clouds. Unlike conventional workflows for cross-source fusion, which end with rigid registration or georeferencing datasets into a common reference frame, this research goes further by extracting PCA-derived local planar features and incorporating them into the fusion process to enhance surface preservation and reduce structural inconsistencies arising from density variations, noise, and acquisition differences. Experimental results demonstrate improved semantic consistency and a more reliable representation of urban features, particularly for buildings and ground surfaces, compared with conventional fusion strategies. What are the main findings? The proposed point-to-plane sequential refinement for the cross-source fusion strategy significantly improved geometric alignment and semantic consistency between airborne LiDAR and DIM point clouds in complex urban environments. Incorporating PCA-based local plane constraints improved surface preservation and reduced registration errors caused by differences in sensor accuracy, noise, and other cross-source discrepancies. What are the implications of the main findings? Improved fusion quality can support more accurate automated 3D city modeling, urban mapping, and semantic segmentation applications using heterogeneous airborne datasets. The proposed framework demonstrates that the local plane constraint in airborne cross-source point cloud fusion provides a robust alternative to conventional fusion methods for large-scale dataset integration and reconstruction. Point cloud fusion is crucial in geospatial analysis, combining data from multiple sources (e.g, LiDAR and photogrammetry) to provide a more complete and accurate environmental representation. However, integrating airborne hybrid sensors or cross-source point clouds remains challenging due to variations in geometric accuracy, data precision, gaps, and sensor attributes. Despite recent advancements, these challenges remain and are among the most demanding aspects in geospatial data processing for remote sensing applications. We propose a new point cloud fusion algorithm that leverages local plane constraints to achieve advanced semantic consistency. The proposed method dynamically fits local planes to the target point clouds, enabling robust alignment of source points to these planes. Evaluation on two real-world datasets demonstrates significant gains in accuracy and preservation of geometric details. Our algorithm also improves the accuracy of downstream tasks such as semantic segmentation. In our experiment, the overall accuracy for the Dudelange dataset increases from 48.5% to 80.1%, and that for the Dublin dataset increases from 72.9% to 88.0%. While challenges persist with sparse and noisy datasets, experimental results highlight the effectiveness of the proposed method, offering valuable insights for maximizing the potential of cross-source point cloud data. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18152598 |
