Commit: 747031222b266da48f9e482a580818ea90e1a9e9
Author: Lukas Stockner
Date: Sun Jul 24 02:12:41 2016 +0200
Branches: soc-2016-cycles_denoising
https://developer.blender.org/rB747031222b266da48f9e482a580818ea90e1a9e9
Cycles: Tweak order of denoising feature vector to improve quality of results
This commit reorders the denoising features: Instead of "Normals, Texture,
Depth, Screen position"
the order now is "Screen position, Depth, Normals, Texture" like in the old
demo code, which significantly
improves result quality.
===================================================================
M intern/cycles/kernel/kernel_filter.h
===================================================================
diff --git a/intern/cycles/kernel/kernel_filter.h
b/intern/cycles/kernel/kernel_filter.h
index ad3d256..01e5f76 100644
--- a/intern/cycles/kernel/kernel_filter.h
+++ b/intern/cycles/kernel/kernel_filter.h
@@ -32,23 +32,23 @@ CCL_NAMESPACE_BEGIN
ccl_device_inline void filter_get_features(int x, int y, float *buffer, float
sample, float *features, float *mean)
{
float sample_scale = 1.0f/sample;
- features[0] = buffer[0] * sample_scale;
- features[1] = buffer[1] * sample_scale;
- features[2] = buffer[2] * sample_scale;
- features[3] = buffer[6] * sample_scale;
- features[4] = buffer[7] * sample_scale;
- features[5] = buffer[8] * sample_scale;
- features[6] = buffer[12] * sample_scale;
- features[7] = x;
- features[8] = y;
+ features[0] = x;
+ features[1] = y;
+ features[2] = buffer[12] * sample_scale;
+ features[3] = buffer[0] * sample_scale;
+ features[4] = buffer[1] * sample_scale;
+ features[5] = buffer[2] * sample_scale;
+ features[6] = buffer[6] * sample_scale;
+ features[7] = buffer[7] * sample_scale;
+ features[8] = buffer[8] * sample_scale;
if(mean) {
for(int i = 0; i < DENOISE_FEATURES; i++)
features[i] -= mean[i];
}
#ifdef DENOISE_SECOND_ORDER_SCREEN
- features[9] = features[7]*features[7];
- features[10] = features[8]*features[8];
- features[11] = features[7]*features[8];
+ features[9] = features[0]*features[0];
+ features[10] = features[1]*features[1];
+ features[11] = features[0]*features[1];
#endif
}
@@ -56,15 +56,15 @@ ccl_device_inline void filter_get_feature_variance(int x,
int y, float *buffer,
{
float sample_scale = 1.0f/sample;
float sample_scale_var = 1.0f/(sample - 1.0f);
- features[0] = saturate(buffer[3] * sample_scale_var) * sample_scale;
- features[1] = saturate(buffer[4] * sample_scale_var) * sample_scale;
- features[2] = saturate(buffer[5] * sample_scale_var) * sample_scale;
- features[3] = saturate(buffer[9] * sample_scale_var) * sample_scale;
- features[4] = saturate(buffer[10] * sample_scale_var) * sample_scale;
- features[5] = saturate(buffer[11] * sample_scale_var) * sample_scale;
- features[6] = saturate(buffer[13] * sample_scale_var) * sample_scale;
- features[7] = 0.0f;
- features[8] = 0.0f;
+ features[0] = 0.0f;
+ features[1] = 0.0f;
+ features[2] = saturate(buffer[13] * sample_scale_var) * sample_scale;
+ features[3] = saturate(buffer[3] * sample_scale_var) * sample_scale;
+ features[4] = saturate(buffer[4] * sample_scale_var) * sample_scale;
+ features[5] = saturate(buffer[5] * sample_scale_var) * sample_scale;
+ features[6] = saturate(buffer[9] * sample_scale_var) * sample_scale;
+ features[7] = saturate(buffer[10] * sample_scale_var) * sample_scale;
+ features[8] = saturate(buffer[11] * sample_scale_var) * sample_scale;
#ifdef DENOISE_SECOND_ORDER_SCREEN
features[9] = 0.0f;
features[10] = 0.0f;
@@ -152,15 +152,13 @@ ccl_device void
kernel_filter_estimate_params(KernelGlobals *kg, int sample, flo
float feature_means[DENOISE_FEATURES] = {0.0f};
FOR_PIXEL_WINDOW {
filter_get_features(px, py, buffer, sample, features, NULL);
- for(int i = 0; i < FEATURE_PASSES; i++)
+ for(int i = 0; i < DENOISE_FEATURES; i++)
feature_means[i] += features[i];
} END_FOR_PIXEL_WINDOW
float pixel_scale = 1.0f / ((high.y - low.y) * (high.x - low.x));
- for(int i = 0; i < FEATURE_PASSES; i++)
+ for(int i = 0; i < DENOISE_FEATURES; i++)
feature_means[i] *= pixel_scale;
- feature_means[7] = x;
- feature_means[8] = y;
/* === Scale the shifted feature passes to a range of [-1; 1], will be
baked into the transform later. === */
float *feature_scale = tempvector;
@@ -168,16 +166,12 @@ ccl_device void
kernel_filter_estimate_params(KernelGlobals *kg, int sample, flo
FOR_PIXEL_WINDOW {
filter_get_features(px, py, buffer, sample, features,
feature_means);
- for(int i = 0; i < FEATURE_PASSES; i++)
+ for(int i = 0; i < DENOISE_FEATURES; i++)
feature_scale[i] = max(feature_scale[i],
fabsf(features[i]));
} END_FOR_PIXEL_WINDOW
- for(int i = 0; i < FEATURE_PASSES; i++)
+ for(int i = 0; i < DENOISE_FEATURES; i++)
feature_scale[i] = 1.0f / max(feature_scale[i], 0.01f);
- feature_scale[7] = feature_scale[8] = 1.0f /
kernel_data.integrator.half_window;
-#ifdef DENOISE_SECOND_ORDER_SCREEN
- feature_scale[9] = feature_scale[10] = feature_scale[11] = 1.0f /
(kernel_data.integrator.half_window*kernel_data.integrator.half_window);
-#endif
@@ -189,17 +183,17 @@ ccl_device void
kernel_filter_estimate_params(KernelGlobals *kg, int sample, flo
math_matrix_zero_lower(feature_matrix, DENOISE_FEATURES);
#ifdef FULL_EIGENVALUE_NORM
float *perturbation_matrix = tempmatrix +
DENOISE_FEATURES*DENOISE_FEATURES;
- math_matrix_zero_lower(perturbation_matrix, DENOISE_FEATURES);
+ math_matrix_zero_lower(perturbation_matrix, FEATURE_PASSES);
#endif
FOR_PIXEL_WINDOW {
filter_get_features(px, py, buffer, sample, features,
feature_means);
- for(int i = 0; i < FEATURE_PASSES; i++)
+ for(int i = 0; i < DENOISE_FEATURES; i++)
features[i] *= feature_scale[i];
math_add_gramian(feature_matrix, DENOISE_FEATURES, features,
1.0f);
filter_get_feature_variance(px, py, buffer, sample, features,
feature_scale);
#ifdef FULL_EIGENVALUE_NORM
- math_add_gramian(perturbation_matrix, DENOISE_FEATURES,
features, 1.0f);
+ math_add_gramian(perturbation_matrix, FEATURE_PASSES, features,
1.0f);
#else
for(int i = 0; i < FEATURE_PASSES; i++)
feature_matrix_norm += features[i];
@@ -214,7 +208,7 @@ ccl_device void kernel_filter_estimate_params(KernelGlobals
*kg, int sample, flo
float *eigenvector_guess = tempvector + 2*DENOISE_FEATURES;
for(int i = 0; i < DENOISE_FEATURES; i++)
eigenvector_guess[i] = 1.0f;
- float singular_threshold = 0.01f + 2.0f *
sqrtf(math_largest_eigenvalue(perturbation_matrix, DENOISE_FEATURES,
eigenvector_guess, tempvector + 3*DENOISE_FEATURES));
+ float singular_threshold = 0.01f + 2.0f *
sqrtf(math_largest_eigenvalue(perturbation_matrix, FEATURE_PASSES,
eigenvector_guess, tempvector + 3*DENOISE_FEATURES));
#else
float singular_threshold = 0.01f + 2.0f * (sqrtf(feature_matrix_norm) /
(sqrtf(rank) * 0.5f));
#endif
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