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# 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 --- 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.