A Parallel Scale-Space Method for Critical Features Tracking on Triangulated Irregular Networks
A scale-space pipeline for terrain represented as Triangulated Irregular Networks (TINs): scale-aware seam-free sampling, an improved TIN smoothing operator, and a fully parallel GPU algorithm for tracking critical points across scales without global sorting.
May 8, 2026
Presentation of Scale Space for TINs @ ACM SIGSPATIAL2024
Oral presentation of our paper "Critical Features Tracking on Triangulated Irregular Networks by a Scale-Space Method" at the 32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Oct 29, 2024
Critical Features Tracking on Triangulated Irregular Networks by a Scale-Space Method
Best Paper Runner-Up at ACM SIGSPATIAL 2024. A scale-space method that identifies and tracks topologically important terrain features directly on Triangulated Irregular Networks, handling irregular point distributions and boundaries that grid-based DEM methods cannot.
Sep 14, 2024
Presentation of ImplicitTerrain @ CVPR2024 INRV Workshop
We present our CVPR 2024 paper "ImplicitTerrain: a Continuous Surface Model for Terrain Data Analysis" at the 1st Workshop on Implicit Neural Representation for Vision
Jun 18, 2024
ImplicitTerrain: a Continuous Surface Model for Terrain Data Analysis
ImplicitTerrain models high-resolution digital terrain continuously and differentiably with an implicit neural representation, enabling accurate surface fitting and parallel topological feature extraction directly from the compact learned model.
Jun 18, 2024