Lens (ERG Volume)

Lens (ERG Volume)

GRG Lens β€” Points β€’ Dual Surface β€’ ERG Volume β€’ Smooth (CΒ²)

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 β†’ Dual surface β†’ 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 (CΒ² 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)), Ξ΅
for each face S∈{S1, S2}:
    compute ΞΊ_max(u,v)  # or, for revolution, ΞΊ_mer(r), ΞΊ_circ(r)
    set h(u,v) ← 2*sqrt(Ξ΅/ΞΊ_max(u,v))
    generate adaptive grid (Ξ”r, Δθ) satisfying h

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

if optical requirements:
    T' ← smooth_MLS_RBF_C2(T) with deviation ≀ Ξ΅/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 β€œsteps”.

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 β€œperfect 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 zβ‚‚ rim
L2 β€” Meridional section (profiles $z_1$, $z_2$) and rim closure.
Adaptive step h(u,v) from ΞΊmax Radial density sketch: smaller step near high curvature regions; circles represent iso-radius samples with varying spacing.
L1 β€” 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 Ξ”u and the grid size K from the dyadic level b, previews the uk values, and exports the grid to CSV.

Inputs

  • Dyadic level b ∈ β„•β‰₯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

  • Ξ”u = β€” (exact: β€”)
  • K = β€” (i.e., number of grid points)
  • N = β€” (turns-first level, N = 2b)
Preview of u-grid uk = k Β· Ξ”u

First 8: β€”

Last 8: β€”

β€”

Practical checklist

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

ERG volume

GRG Sphere β€” Points β€’ Surface β€’ ERG Volume β€’ 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 β†’ Surface β†’ 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, Ξ΅
ΞΊ ← 1/R
h_max ← 2*sqrt(Ξ΅/ΞΊ)       # = 2*sqrt(Ξ΅R)
N    ← round((2Ο€/√3) * R/Ξ΅)

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') ≀ Ξ΅/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 β†’ surface β†’ ERG volume β†’ smooth at scale $\varepsilon$ sets the general GRG/ERG procedure for solids.

7) Figures (inline SVG)

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

8) FAQ

Is β€œperfect” 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 Ξ”u and the grid size K from the dyadic level b, previews the uk values, and exports the grid to CSV.

Inputs

  • Dyadic level b ∈ β„•β‰₯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

  • Ξ”u = β€” (exact: β€”)
  • K = β€” (i.e., number of grid points)
  • N = β€” (turns-first level, N = 2b)
Preview of u-grid uk = k Β· Ξ”u

First 8: β€”

Last 8: β€”

β€”

Practical checklist

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