A PROCEDURAL METHOD FOR GENERATING PBR TEXTURE MAPS FROM HISTORICAL GEOMETRIC PATTERNS

This paper proposes a procedural method for generating PBR texture maps from a parametric geometric model of a historical pattern. A single geometric mask is used as a common spatial source for the Base Color, Height, Normal, and Roughness maps, while surface micro-variations are represented by a separately controlled deterministic function. A reproducible 1024 × 1024 prototype was implemented using Python and NumPy to verify cross-map geometric consistency, normal-vector normalisation, and sensitivity to changes in a geometric parameter. The experimental results confirm the consistency and functionality of the proposed computational pipeline, while further physical calibration is required before the generated maps can be interpreted as representations of measured properties of historical materials.

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-08
DOI
https://doi.org/10.5281/zenodo.23235709
Primary Topic
Computer Graphics and Visualization Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A PROCEDURAL METHOD FOR GENERATING PBR TEXTURE MAPS FROM HISTORICAL GEOMETRIC PATTERNS

Yunusova Dilnoza Alimjan qizi, Saida Safibullayevna Beknazarova
Zenodo (CERN European Organization for Nuclear Research)
Computer Graphics and Visualization Techniques
article

A PROCEDURAL METHOD FOR GENERATING PBR TEXTURE MAPS FROM HISTORICAL GEOMETRIC PATTERNS

Yunusova Dilnoza Alimjan qizi, Saida Safibullayevna Beknazarova
article en

Abstract

This paper proposes a procedural method for generating PBR texture maps from a parametric geometric model of a historical pattern. A single geometric mask is used as a common spatial source for the Base Color, Height, Normal, and Roughness maps, while surface micro-variations are represented by a separately controlled deterministic function. A reproducible 1024 × 1024 prototype was implemented using Python and NumPy to verify cross-map geometric consistency, normal-vector normalisation, and sensitivity to changes in a geometric parameter. The experimental results confirm the consistency and functionality of the proposed computational pipeline, while further physical calibration is required before the generated maps can be interpreted as representations of measured properties of historical materials.

Zenodo (CERN European Organization for Nuclear Research)
Tashkent University of Information Technology (UZ)
Openalex Percentile: Top 4%
Computer Graphics and Visualization Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.