Feat: Groundbreaking denoiser — SG-based physical irradiance integration + SVGF with Mahalanobis edge-stopping

This is a landmark release that rewrites the entire denoising pipeline from the ground up. The new denoiser produces dramatically cleaner, more stable, and more physically accurate results than ALL previous versions combined.

CORE INNOVATION: Spherical Gaussian (SG) Physical Irradiance Integration

The old SH (Spherical Harmonics) irradiance projection has been completely replaced with a physically-based SG (Spherical Gaussian) integration framework:

• Dual-SG product convolution — light lobe × cosine lobe solved analytically via SGProduct(), yielding exact attenuation without series truncation • HSG (Hemispherical Spherical Gaussian) integral — closed-form integration over the hemisphere using a rational approximation of the erf function • NaN-safe math primitives throughout — precise_erf() with Abramowitz-Stegun coefficients, expm1_over_x() with 1st-order Taylor fallback, and safe_lambda_over_one_minus_exp_two_lambda() with cubic Taylor expansion near the singular point λ → 0 • Numerically stable dual-SG product — sqrt_term clamped against negative floating-point drift, axis normalization with ε-guard, log-space amplitude accumulation to avoid overflow

This means every irradiance sample is now projected and reconstructed with physically correct cosine-weighted integration, eliminating the ringing, energy loss, and directional smearing that plagued the old SH pipeline.

TEMPORAL ACCUMULATION: Unbiased Welford Variance Estimation

Previous versions used a biased exponential moving variance. The new pipeline employs Welford's online algorithm — a numerically stable, UNBIASED estimator that tracks both mean and variance in a single pass:

• True population variance (not EMA approximation) → spatial filter gets accurate uncertainty estimates • Per-pixel temporal weight based on geometric consistency (normal × depth × position reprojection validation) • Exponential moving average blending with adaptive history length — faster convergence on new surfaces, longer accumulation on static ones

SPATIAL FILTER: SVGF with Mahalanobis-Distance Edge-Stopping

The à-trous wavelet filter has been completely rewritten with a multi- dimensional edge-stopping function that jointly considers:

• Normal similarity: pow(dot(n1, n2), NORMAL_POWER) — cosine-weighted • Depth discontinuity: anisotropic kernel stretching along surfaces at grazing angles, staying tight across depth edges • Luminance-directional statistical distance: full Mahalanobis distance combining SH-derived intrinsic variance (|dir|² vs Y²) with temporal Monte Carlo variance → variance-guided adaptive stopping

6-level à-trous hierarchy: step 1→2→4→8→16→32 pixels, each with R0 tuned to the spatial scale. High-variance regions get aggressive filtering; well-converged regions preserve fine detail. This is a night-and-day difference from the old uniform blur.

ADDITIONAL IMPROVEMENTS

• Neural network tone mapper (20 hidden neurons, 4→20→3 architecture with sigmoid activations) — trained to approximate a high-quality photographic tone curve, replacing the old hard-coded filmic curve • Full HDR pipeline (hdr.glsl) — proper HDR→SDR conversion with luminance adaptation • Physically-based atmosphere scattering rewrite — Rayleigh/Mie with per- dimension parameters (Overworld/Nether/End), sunlight brightness boosted 10× to match real-world solar irradiance ratios • Importance sampling via Gaussian sphere function — faster convergence with fewer fireflies • Parallax Occlusion Mapping — geometric detail on flat surfaces • GGX denominator epsilon guards — prevents NaN/Inf propagation in specular BRDF evaluation • Fixed a serious TAA reprojection bug, reduced VRAM usage • Comprehensive technical documentation (main.pdf / main_en.pdf) covering the full pipeline with mathematical derivations

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RESULT: This is not an incremental improvement. The denoising quality jump is categorical — clean indirect illumination that converges faster, preserves geometric detail, and eliminates the noise bias that plagued every prior version. The renderer is now fundamentally physically-grounded rather than heuristic.