Implement simple screen-space blue-noise diffusion sampling.
From the paper "Screen-Space Blue-Noise Diffusion of Monte Carlo Sampling Error via Hierarchical Ordering of Pixels" by Ahmed et al.
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Cargo.lock
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Cargo.lock
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@ -589,9 +589,9 @@ checksum = "1d51f5df5af43ab3f1360b429fa5e0152ac5ce8c0bd6485cae490332e96846a8"
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[[package]]
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name = "sobol_burley"
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version = "0.3.0"
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version = "0.4.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "26e3528b09b1f1b1e152342a4462d1e80d568dc5623a0772252a6e584a53d550"
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checksum = "3441b32bbb896e372f1b8e7eb51a983713aef99599c32c0eb69183aa490cb6a0"
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[[package]]
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name = "spectral_upsampling"
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@ -30,13 +30,15 @@ nom = "5"
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num_cpus = "1.8"
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openexr = "0.7"
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kioku = "0.3"
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sobol_burley = "0.3"
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sobol_burley = "0.4"
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png_encode_mini = "0.1.2"
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rustc-serialize = "0.3"
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scoped_threadpool = "0.1"
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time = "0.1"
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fastapprox = "0.3"
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# Local crate dependencies
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[dependencies.bvh_order]
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path = "sub_crates/bvh_order"
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@ -240,8 +240,10 @@ impl<'a> Renderer<'a> {
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for x in bucket.x..(bucket.x + bucket.w) {
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for si in 0..self.spp {
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// Raw sample numbers.
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let (d0, d1, d2, d3) = get_sample_4d(si as u32, 0, (x, y), self.seed);
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let (d4, _, _, _) = get_sample_4d(si as u32, 1, (x, y), self.seed);
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let (d0, d1, d2, d3) =
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get_sample_4d(si as u32, 0, (x, y), self.seed, self.spp as u32);
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let (d4, _, _, _) =
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get_sample_4d(si as u32, 1, (x, y), self.seed, self.spp as u32);
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// Calculate image plane x and y coordinates
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let (img_x, img_y) = {
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@ -262,9 +264,12 @@ impl<'a> Renderer<'a> {
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d0,
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map_0_1_to_wavelength(golden_ratio_sample(
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si as u32,
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hash_u32((x << 16) ^ y, self.seed),
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self.seed,
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(x, y),
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self.spp as u32,
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)),
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si as u32,
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self.spp as u32,
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);
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paths.push(path);
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rays.push(ray, false);
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@ -383,6 +388,8 @@ pub struct LightPath {
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light_attenuation: Float4,
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pending_color_addition: Float4,
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color: Float4,
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max_samples: u32,
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}
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#[allow(clippy::new_ret_no_self)]
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@ -396,6 +403,7 @@ impl LightPath {
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time: f32,
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wavelength: f32,
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sample_number: u32,
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max_samples: u32,
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) -> (LightPath, Ray) {
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(
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LightPath {
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@ -416,6 +424,8 @@ impl LightPath {
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light_attenuation: Float4::splat(1.0),
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pending_color_addition: Float4::splat(0.0),
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color: Float4::splat(0.0),
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max_samples: max_samples,
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},
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scene.camera.generate_ray(
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image_plane_co.0,
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@ -441,6 +451,7 @@ impl LightPath {
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dimension,
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self.pixel_co,
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self.sampling_seed,
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self.max_samples,
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)
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}
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@ -698,15 +709,22 @@ fn get_sample_4d(
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dimension_set: u32,
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pixel_co: (u32, u32),
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seed: u32,
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max_samples: u32,
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) -> (f32, f32, f32, f32) {
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// A unique seed for every pixel coordinate up to a resolution of
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// 65536 x 65536. Also incorperating the seed.
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let seed = pixel_co.0 ^ (pixel_co.1 << 16) ^ seed.wrapping_mul(0x736caf6f);
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match dimension_set {
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ds if ds < sobol_burley::NUM_DIMENSION_SETS_4D as u32 => {
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// Sobol sampling.
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let n4 = sobol_burley::sample_4d(i, ds, seed);
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//
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// The `offset` is for screen-space blue noise distribution in the
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// spirit of the paper "Screen-Space Blue-Noise Diffusion of Monte
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// Carlo Sampling Error via Hierarchical Ordering of Pixels" by
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// Ahmed et al.
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// `sample_4d()` already does sample shuffling internally in a way
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// consistent with the paper, so we only have to arrange the pixels
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// according to a space filling curve to achieve the results from
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// the paper.
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let offset = hilbert::xy2d(pixel_co.0, pixel_co.1) * max_samples;
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let n4 = sobol_burley::sample_4d(offset + i, ds, seed);
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(n4[0], n4[1], n4[2], n4[3])
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}
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ds => {
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@ -723,15 +741,30 @@ fn get_sample_4d(
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}
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/// Golden ratio sampler.
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fn golden_ratio_sample(i: u32, scramble: u32) -> f32 {
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// NOTE: use this for the wavelength dimension, because
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// due to the nature of hero wavelength sampling this ends up
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// being crazily more efficient than pretty much any other sampler,
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// and reduces variance by a huge amount.
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let n = i
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.wrapping_add(hash_u32(scramble, 0))
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.wrapping_mul(2654435769);
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n as f32 * (1.0 / (1u64 << 32) as f32)
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///
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// NOTE: use this for the wavelength dimension, because
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// due to the nature of hero wavelength sampling this ends up
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// being crazily more efficient than pretty much any other sampler,
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// and reduces variance by a huge amount.
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fn golden_ratio_sample(i: u32, seed: u32, pixel_co: (u32, u32), max_samples: u32) -> f32 {
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use sobol_burley::parts::{owen_scramble_rev, u32_to_f32_norm};
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// Turns out the screen-space blue-noise diffusion trick mentioned
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// in `get_sample_4d()` works reasonably well on golden ratio
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// sampling as well! But we actually have to do the shuffling
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// ourselves here.
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let offset = owen_scramble_rev(
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hilbert::xy2d(pixel_co.0, pixel_co.1).reverse_bits(),
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hash_u32(seed, 6543266),
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)
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.reverse_bits()
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* max_samples;
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let i = i.wrapping_add(offset);
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// The actual golden ratio sampling computation.
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let n = i.wrapping_mul(2654435769);
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u32_to_f32_norm(n)
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}
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#[derive(Debug)]
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