2026-10-15T17:15:16Z https://data.mendeley.com/oai

oai:data.mendeley.com/pdhnmvt46r.1 2026-09-11T00:07:53Z

Ceballos, Johan Data and Python code for angular collision diversity in three-dimensional point clouds Mendeley Data Python code and numerical results for finite-cloud angular collision diversity at a stated resolution. The deposit includes six-family simulations, bandwidth sensitivity, exact-target calibration, sufficient stability-bound checks, comparisons with binned angular collision and a scalar distance kernel, and public illustrations using Stanford Bunny, Armadillo and Drill. The angular kernel has smaller median perturbation changes in the specified comparison, while the distance kernel has higher held-out classification accuracy. Public-object analyses distinguish full-vertex sampling, uniform subsets, noisy subsets and density-biased subsets. Original Stanford models are not redistributed; verified downloaders and source checksums identify the inputs. Repeated numerical runs are not independent physical objects. Conditional Monte Carlo uncertainty does not establish population-sampling coverage. Three-Dimensional Graphics Monte Carlo Method Collision Simulation Popayán-Hernández, Juan Guillermo Saurith, Juan Dataset doi:10.17632/pdhnmvt46r.1 oai:data.mendeley.com/pdhnmvt46r.1 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/pdhnmvt46r 2026-09-11T00:07:53Z