Papers on Kittipong Tapyou

Comparative Evaluation of Point Cloud Upsampling Networks for Real-World LiDAR Terrain Reconstruction

Tue, 25 Aug 2026 00:00:00 +0000

Abstract

Airborne LiDAR point clouds represent terrain but are often sparse, noisy, and unevenly distributed, limiting their use in surface reconstruction and geospatial analysis. This study benchmarks four learning-based point cloud upsampling architectures, PU-Net, MPU, PU-GCN, and PU-Transformer, using real-world terrain data from the U.S. Geological Survey 3D Elevation Program (3DEP). A unified Farthest Point Sampling (FPS)-based preprocessing pipeline generates input and ground-truth patch pairs. Each model is trained with a fixed 4× upsampling ratio and input patch sizes of 128, 256, 512, and 1024 points. Performance is assessed on a held-out terrain sub-region using Chamfer Distance, Hausdorff Distance, Point-to-Surface Distance, Earth Mover’s Distance, and the Normalized Uniformity Coefficient. MPU achieves the best mean surface fidelity and point uniformity, obtaining the lowest Chamfer Distance, Point-to-Surface Distance, and Normalized Uniformity Coefficient at 1024 input points. PU-GCN achieves the lowest Hausdorff Distance and Earth Mover’s Distance at 512 input points. PU-Transformer remains competitive, whereas PU-Net produces consistently larger errors and does not benefit reliably from denser inputs. The three more recent architectures generally improve as patch capacity increases, indicating that richer local context improves terrain reconstruction. Overall, MPU and PU-GCN are the most suitable approaches for real-world LiDAR terrain point cloud upsampling tasks.

Patch-Based Bézier Surface Upsampling of Gridded Rainfall Data in the Chao Phraya River Basin, Thailand

Tue, 25 Aug 2026 00:00:00 +0000

Abstract

High-resolution rainfall data are essential for basin-scale hydrological analysis, but long-term rainfall products are often available only at coarse spatial resolutions. This study proposes a patch-based upsampling method using Bézier surfaces to enhance the spatial resolution of gridded rainfall data over the Chao Phraya River Basin, Thailand. Daily rainfall observations from 79 Thai Meteorological Department stations during 1981 – 2021 were quality controlled and interpolated using Inverse Distance Weighting (IDW) to generate 15-arc-minute rainfall grids. Each coarse grid was subdivided into rectangular patches based on local rainfall peak locations. A tensor-product Bézier surface was fitted to each patch and sampled to produce a 5-arc-minute rainfall field. The proposed method was evaluated using spatial comparisons and monthly basin-average rainfall over the Chao Phraya River Basin. The upsampled fields preserved the broad spatial rainfall pattern of the coarse input while providing smoother rainfall gradients at the target resolution. Monthly basin-average rainfall from the Bézier -upsampled fields showed near-perfect agreement with the directly interpolated 5-arc-minute IDW reference field, with R^2=0.999 for 1981 – 2021. Strong agreement was also obtained with the T12 dataset (R^2=0.981) and K10 dataset (R^2=0.844), whereas agreement with the Thiessen polygon dataset was moderate (R^2=0.663). The results

Hole Filling for 3D Point Clouds via Bézier Curve with C1-Continuity Generation

Sat, 01 Nov 2025 00:00:00 +0000

We reconstruct incomplete point clouds using C1-continuous cubic Bézier curves for accurate, smooth surface completion.

A Novel Triangulation-Based Method for Hole Filling in 3D Point Clouds Using Approximation Curve Fitting

Fri, 31 Oct 2025 00:00:00 +0000

We reconstruct missing point-cloud regions using triangulated control-point curves and uniform sampling for accurate surface completion.

Smart School Attendance System using Face Recognition with Near Optimal Imaging

Tue, 27 Jul 2021 00:00:00 +0000

Face-recognition attendance system using web and IoT improves accuracy by optimizing student position and distance..