Lens (ERG Volume)

Lens (ERG Volume)

GRG Lens — Points • Dual Surface • ERG Volume • Smooth (C²)

Abstract. A lens is the envelope of two surfaces (front $S_1$, back $S_2$) with a shared rim. At resolution $\varepsilon$, the local step obeys:

$$ h(u,v) \;\le\; 2\,\sqrt{\frac{\varepsilon}{\kappa_{\max}(u,v)}}, $$

We build the closed solid and compute the ERG volume, then apply Smooth$_\varepsilon$ with $C^{2}$ continuity to avoid optical edges.

1) Definitions (curvatures & adaptive step)

  • Principal curvatures $(\kappa_1,\kappa_2)$; $\kappa_{\max}=\max(|\kappa_1|,|\kappa_2|)$.
  • Local step $h(u,v)$: imposed by $2\sqrt{\varepsilon/\kappa_{\max}}$.
  • Perfect at scale $\varepsilon$: geometric deviation $\le \varepsilon$ on the whole lens.

Axisymmetric case $z=f(r)$

$$ \kappa_{\text{mer}}=\frac{f''(r)}{\bigl(1+f'(r)^{2}\bigr)^{3/2}}, \qquad \kappa_{\text{circ}}=\frac{f'(r)}{r\,\sqrt{1+f'(r)^{2}}}, \qquad \kappa_{\max}=\max\!\bigl(|\kappa_{\text{mer}}|,|\kappa_{\text{circ}}|\bigr). $$

2) Points → Dual surface → ERG volume

2.1 Points (adaptive radial–angular grid)

Sample $S_1$ and $S_2$ with $\Delta r$, $\Delta \theta$ such that local geodesic spacing never exceeds $h(u,v)$. Density increases where $\kappa_{\max}$ is higher (small radii or high-curvature regions).

2.2 Surfaces and closure

Build two coherent meshes $S_1, S_2$ and close with a thin lateral ring (cylindrical rim) to obtain a closed, oriented solid.

2.3 ERG volume

Tetrahedra method (as for the sphere) on the closed mesh:

$$ V_{\mathrm{ERG}}=\frac{1}{6}\sum_{\triangle ijk}\bigl((p_i\times p_j)\cdot p_k\bigr). $$

If axisymmetric, cylindrical shells give a consistent Riemann sum:

$$ V_{\mathrm{ERG}}\;\approx\; 2\pi\sum_{k}\; t(r_k)\, r_k\,\Delta r, \qquad t(r)=z_1(r)-z_2(r), $$

which matches the tetrahedra estimate with $O(h^{2})$ error when the sampling bound is enforced.

3) Smooth$_{\varepsilon}$ for optics (C² continuity)

Goal: remove micro-faceting and guarantee surface and derivative continuity ($C^{2}$) for stable normals and curvature.

  1. Local MLS/RBF fit (degree $\ge 2$) on a radius $\rho\in[h,2h]$ for each face.
  2. Resample vertices on the smoothed patches; deviation $\le \varepsilon/2$.
  3. Normal filtering along geodesic neighborhoods, bandwidth $\propto \varepsilon$.
  4. Optical check: angular variation of normals consistent with expected faceting and no edges (no $C^{1}$ discontinuities).
  5. Topology check: no self-intersections after smoothing.

4) Algorithm (concise)

input: surfaces S1, S2 (or z1=f1(r), z2=f2(r)), ε
for each face S∈{S1, S2}:
    compute κ_max(u,v)  # or, for revolution, κ_mer(r), κ_circ(r)
    set h(u,v) ← 2*sqrt(ε/κ_max(u,v))
    generate adaptive grid (Δr, Δθ) satisfying h

T ← triangulate(S1) ∪ triangulate(S2) ∪ cylindrical_rim
orient(T)
A ← sum_areas(T)
V_ERG ← tetra_volume(T)

if optical requirements:
    T' ← smooth_MLS_RBF_C2(T) with deviation ≤ ε/2
    check_normals(T')          # continuity & angular variation
    check_self_intersections(T')
output: (closed mesh T', A, V_ERG, normals)

5) Checks & validation

  • Hausdorff (ideal ↔ mesh) $\le \varepsilon$.
  • $C^{2}$ continuity after smoothing (tests on numerical derivatives of patches).
  • Triangle quality and no self-intersections.
  • (Optional) qualitative optical test: no “steps”.

6) Why it matters

Lenses require $C^{2}$ continuity: even tiny edges degrade refraction. GRG adaptive sampling densifies where needed; Smooth$_\varepsilon$ controls deviation. The result is a lens “perfect at the chosen resolution”, with validated ERG volume.

7) Figures (inline SVG)

Meridional section: z1(r), z2(r) and cylindrical rim Two smooth profiles forming a biconvex lens; the rim is closed with a short cylinder to make a watertight solid. z r z₁ z₂ rim
L2 — Meridional section (profiles $z_1$, $z_2$) and rim closure.
Adaptive step h(u,v) from κmax Radial density sketch: smaller step near high curvature regions; circles represent iso-radius samples with varying spacing.
L1 — Density map driven by $\kappa_{\max}$: smaller $h$ where curvature is higher.

8) FAQ

How do I choose $\varepsilon$ for optics?
Start from the required form tolerance (e.g., $\lambda/10$ or microns) and translate it into a geometric deviation limit.

Does smoothing alter dioptric power?
No, if capped at $\varepsilon/2$ and validated with deviation maps and derivative checks.

What about the rim?
Close with a coherent ring: no sharp or inverted edges; enforce correct orientation.

ERG Quick Calculator ERG grid icon Concentric arcs with dyadic ticks representing ERG refinement.

This compact tool computes the quarter-sampling step Δu and the grid size K from the dyadic level b, previews the uk values, and exports the grid to CSV.

Inputs

  • Dyadic level b ∈ ℕ≥0 (recommended range: 0–18)
  • Optional: recent estimates V2b-1 and V2b to check the dyadic stopping rule
\[ \Delta u \;=\; 2^{-(b+2)}, \qquad K \;=\; \left\lfloor \frac{1}{\Delta u} \right\rfloor \;=\; 2^{\,b+2}. \]
\[ \text{Stop if}\quad \bigl|V_{2^{b}} - V_{2^{b-1}}\bigr| \le \varepsilon. \]

Calculator

Results

  • Δu = — (exact: —)
  • K = — (i.e., number of grid points)
  • N = — (turns-first level, N = 2b)
Preview of u-grid uk = k · Δu

First 8: —

Last 8: —

—

Practical checklist

  • Use dyadic refinement: increase b until the dyadic difference falls below ε.
  • Quarter-sampling is assumed: Δu = 2-(b+2).
  • Grid is deterministic; CSV can be versioned for reproducibility.
ERG volume

ERG volume

GRG Sphere — Points • Surface • ERG Volume • Smooth

Abstract. A sphere of radius $R$ is generated as a quasi-uniform point set. With sampling step:

$$ h_{\max}(\kappa,\varepsilon)\;=\;2\sqrt{\frac{\varepsilon}{\kappa}}, \qquad \kappa=\frac{1}{R}, $$

the reconstructed surface is perfect at scale $\varepsilon$ (geometric error $\le \varepsilon$); area and ERG volume converge with $O(h^2)$ error.

1) Definitions

  • Resolution $\varepsilon$: admissible indistinguishability threshold.
  • Step $h$: distance between nearby surface samples.
  • Curvature $\kappa$: for a sphere, $\kappa = 1/R$.
  • Perfect at scale $\varepsilon$: Hausdorff distance (ideal ↔ reconstructed) $\le \varepsilon$.

Geometric reminder (sagitta of an arc of radius $R$):

$$ s \;\approx\; \frac{h^{2}}{8R} \;=\; \frac{\kappa h^{2}}{8} $$
$$ \Rightarrow\quad h_{\max} \;=\; 2\sqrt{\frac{\varepsilon}{\kappa}} \;=\; 2\sqrt{\varepsilon R}. $$

2) Points → Surface → ERG volume

2.1 Points (quasi-uniform sampling)

  • Spherical Fibonacci lattice: simple and isotropic.
  • Icosphere (subdivided icosahedron): regular connectivity.

Point count (triangles near-equilateral, area $(\sqrt{3}/2)\,h^2$):

$$ N \;\approx\; \frac{4\pi R^{2}}{(\sqrt{3}/2)\,h^{2}} \;=\; \frac{2\pi}{\sqrt{3}}\,\frac{R}{\varepsilon}. $$

It grows linearly with $R/\varepsilon$.

2.2 Surface (closed, oriented mesh)

Triangulate coherently on the spherical manifold. Face normal: $\mathbf{n}_f \propto (p_j-p_i)\times(p_k-p_i)$.

Discrete area:

$$ A \;=\; \sum_{\triangle ijk} \frac{1}{2}\,\big\|\, (p_j-p_i)\times(p_k-p_i)\, \big\|. $$

2.3 ERG volume (from closed surface)

With an oriented closed mesh (tetrahedra w.r.t. the origin):

$$ V_{\mathrm{ERG}} \;=\; \frac{1}{6}\sum_{\triangle ijk}\big((p_i\times p_j)\cdot p_k\big). $$

Convergence: $A$ and $V_{\mathrm{ERG}}$ approach the ideal values with $O(h^2)$ error.

3) Smooth$_{\varepsilon}$ (when and how)

Goal: stabilize normals and reflections without exceeding $\varepsilon$.

  1. Local Taubin/MLS filter (forward/backward pass to avoid shrinkage).
  2. Projection onto the sphere ($\lVert v\rVert \to R$).
  3. Check: Hausdorff (pre/post) $\le \varepsilon/2$.
  4. Smoothed normals from local MLS fit ($\ge C^{1}$ continuity).

When it’s not needed: if you only measure area/ERG volume and faceting is already below the $\varepsilon$-scale threshold.

4) Algorithm (concise)

input: R, ε
κ ← 1/R
h_max ← 2*sqrt(ε/κ)       # = 2*sqrt(εR)
N    ← round((2π/√3) * R/ε)

P ← fibonacci_sphere(N)    # or icosphere(h_max)
T ← triangulate_on_sphere(P)
orient(T)                  # outward normals

A ← sum_areas(T)
V_ERG ← tetra_volume(T)

if need_smooth_normals:
    P' ← taubin_mls(P, T)  # controlled smoothing
    P' ← project_to_sphere(P', R)
    assert hausdorff(P, P') ≤ ε/2
    recompute_normals(P', T)
output: (P, T, A, V_ERG, normals)

5) Checks & validation

  • Hausdorff distance (ideal ↔ mesh) $\le \varepsilon$.
  • Triangle quality (avoid extreme angles).
  • No self-intersections.
  • $O(h^{2})$ convergence for area and ERG volume.

6) Why it matters

The sphere is an ideal testbed: constant curvature and closed-form $A,V$. The chain points → surface → ERG volume → smooth at scale $\varepsilon$ sets the general GRG/ERG procedure for solids.

7) Figures (inline SVG)

Sagitta geometry: s ≈ h^2/(8R) Arc of radius R, chord of length h, sagitta s; all lengths in the same units. R O s | ⟂ h
F3 — Sagitta relation used to bound $h_{\max}$.
Quasi-uniform points on a sphere Wireframe sphere with dots to suggest Fibonacci vs icosphere distributions.
F1 — Point distribution (Fibonacci vs icosphere idea) at equal $h$.

8) FAQ

Is “perfect” absolute?
No: it is perfect at scale $\varepsilon$. Details $<\,\varepsilon$ are not guaranteed.

Do we always need smoothing?
No: needed for normals/optics stability; otherwise optional.

Fibonacci or icosphere?
Both work: Fibonacci is simple and isotropic; icosphere offers regular connectivity.

How do I choose $\varepsilon$?
Based on use: measurement accuracy, rendering, fabrication, etc.

ERG Quick Calculator ERG grid icon Concentric arcs with dyadic ticks representing ERG refinement.

This compact tool computes the quarter-sampling step Δu and the grid size K from the dyadic level b, previews the uk values, and exports the grid to CSV.

Inputs

  • Dyadic level b ∈ ℕ≥0 (recommended range: 0–18)
  • Optional: recent estimates V2b-1 and V2b to check the dyadic stopping rule
\[ \Delta u \;=\; 2^{-(b+2)}, \qquad K \;=\; \left\lfloor \frac{1}{\Delta u} \right\rfloor \;=\; 2^{\,b+2}. \]
\[ \text{Stop if}\quad \bigl|V_{2^{b}} - V_{2^{b-1}}\bigr| \le \varepsilon. \]

Calculator

Results

  • Δu = — (exact: —)
  • K = — (i.e., number of grid points)
  • N = — (turns-first level, N = 2b)
Preview of u-grid uk = k · Δu

First 8: —

Last 8: —

—

Practical checklist

  • Use dyadic refinement: increase b until the dyadic difference falls below ε.
  • Quarter-sampling is assumed: Δu = 2-(b+2).
  • Grid is deterministic; CSV can be versioned for reproducibility.
ERG – Trapezoidal

ERG – Trapezoidal

Document Status and Current Reference

This page preserves an earlier formulation or research method. Its notation, formulas, and embedded demonstrations belong to that version and should not be treated as the specification of the current binary radial kernels.

Read the current formulation and its analytical verification. Historical performance statements and implementation behaviour require separate validation. Earlier references to circular constants remain in this historical document; they are not inputs to the current geometric kernel definitions.

Δx Trapezoidal (Cartesian)

Area from the inside on the quarter of a circle. Sample uniformly on the x-axis with $\Delta x=r/N$, build trapezoids strictly under the arc $y(x)=\sqrt{r^2-x^2}$, then multiply by 4 for the full circle. No angles and no external constants appear in the primary operators.

Setup (quarter)

$$ \Delta x=\frac{r}{N},\qquad x_k=k\,\Delta x\ \ (k=0,\dots,N),\qquad y_k=\sqrt{\,r^2-x_k^2\,}. $$

Quarter area (→ ×4 for the circle)

$$ A_{\text{quarter}}(N)\;=\;\sum_{k=0}^{N-1}\frac{\Delta x}{2}\,\bigl(y_k+y_{k+1}\bigr), \qquad A_{\text{circle}}(N)\;=\;4\,A_{\text{quarter}}(N). $$

Because $y(x)$ is concave on $[0,r]$, these trapezoids give a monotone under-approximation. The error typically decays like $O(N^{-2})$. A quick check is step-doubling: compare $A_{\text{circle}}(N)$ vs $A_{\text{circle}}(2N)$.

Quarter with Δx trapezoids (Cartesian x–y) Strict Cartesian axes (equal units). r=100, N=5 ⇒ Δx=20. Trapezoids are inside the quarter arc. x y
Fig. 1 — Δx trapezoids strictly inside the quarter arc (example: $r=100$, $N=5$).

Single strip geometry

Between $x_k$ and $x_{k+1}$ the trapezoid has bases $y_k$ and $y_{k+1}$ and width $\Delta x$; the arc segment lies strictly above it.

One Δx trapezoid under the arc Strip between x=60 and x=80 for r=100. Heights are y_k=80 and y_{k+1}=60. Dashed arc segment shown above the trapezoid. x y
Fig. 2 — One Δx strip (here $k=3$): the arc is strictly above the trapezoid.

Pseudocode

Input: r, N
Δx = r / N
Aq = 0
for k = 0..N-1:
    yk   = sqrt(r*r - (k*Δx)*(k*Δx))
    yk1  = sqrt(r*r - ((k+1)*Δx)*((k+1)*Δx))
    Aq  += 0.5 * Δx * (yk + yk1)
A_circle = 4 * Aq
  

Notes

  • Monotone: $A_{\text{circle}}(N)$ increases with $N$ and approaches the true area from below.
  • Accuracy: smooth $O(N^{-2})$ decay; step-doubling is a simple diagnostic.
  • Implementation: works cleanly with fixed-point or vectorized code (just sums and square roots).