True Spherenetics - A Unified Scale-Invariant

For nearly a century, 3D morphological characterization relied on decoupled geometrical metrics first introduced by Hakon Wadell: volumetric macro sphericity (\Psi, 1932) and projected micro roundness (1935). While useful for sedimentological classification, the lack of a unified scalar state metric and a formal Greek symbol twin for local curvature smoothness left a fundamental void in multi-scale spatial analysis. This paper formally introduces Sphereocity (\Phi) as the direct local counterpart to sphericity (\Psi), establishing the dimensionless scalar identity True Spherenetic (\Sigma_{\text{True}} = \Psi \cdot \Phi). By unifying global volumetric compactness with local sub-voxel boundary smoothness into a single scale-invariant operator (0 < \Sigma \le 1.0), we resolve scale-mismatch anomalies inherent in traditional single-tier metrics. Additionally, we define a complete morphological arithmetic operator suite—comprising the Sharpness Index (\Xi = \Psi / \Phi), Elongation Factor (\Theta = \Phi / \Psi), and Morphological Bias (\Delta = \Psi - \Phi). We evaluate analytical bounds across Euclidean continuous manifolds, regular Platonic polyhedra (Tetralis, Hexalis, Octalis, Pentalis, Icosalis), discretized voxel lattice meshes, and relativistic spacetime boundaries (Schwarzschild and extremal Kerr black hole horizons). True Spherenetic provides a scale-invariant framework for spatial fidelity across computational geometry, discrete lattice physics, and theoretical cosmology.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-26
DOI
https://doi.org/10.5281/zenodo.23633279
Primary Topic
Digital Image Processing Techniques
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

True Spherenetics - A Unified Scale-Invariant

Google Gemini (AI), Austin Neely
1 links
Zenodo (CERN European Organization for Nuclear Research)
Digital Image Processing Techniques
preprint

True Spherenetics - A Unified Scale-Invariant

Google Gemini (AI), Austin Neely
preprint en

Abstract

For nearly a century, 3D morphological characterization relied on decoupled geometrical metrics first introduced by Hakon Wadell: volumetric macro sphericity (\Psi, 1932) and projected micro roundness (1935). While useful for sedimentological classification, the lack of a unified scalar state metric and a formal Greek symbol twin for local curvature smoothness left a fundamental void in multi-scale spatial analysis. This paper formally introduces Sphereocity (\Phi) as the direct local counterpart to sphericity (\Psi), establishing the dimensionless scalar identity True Spherenetic (\Sigma_{\text{True}} = \Psi \cdot \Phi). By unifying global volumetric compactness with local sub-voxel boundary smoothness into a single scale-invariant operator (0 < \Sigma \le 1.0), we resolve scale-mismatch anomalies inherent in traditional single-tier metrics. Additionally, we define a complete morphological arithmetic operator suite—comprising the Sharpness Index (\Xi = \Psi / \Phi), Elongation Factor (\Theta = \Phi / \Psi), and Morphological Bias (\Delta = \Psi - \Phi). We evaluate analytical bounds across Euclidean continuous manifolds, regular Platonic polyhedra (Tetralis, Hexalis, Octalis, Pentalis, Icosalis), discretized voxel lattice meshes, and relativistic spacetime boundaries (Schwarzschild and extremal Kerr black hole horizons). True Spherenetic provides a scale-invariant framework for spatial fidelity across computational geometry, discrete lattice physics, and theoretical cosmology.

Zenodo (CERN European Organization for Nuclear Research)
Digital Image Processing 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.

Laika access coming soon