Commit: d23f00039f1e953ed5ca0dac3ceccc07230e7170
Author: Lukas Stockner
Date: Tue Aug 9 02:22:59 2016 +0200
Branches: soc-2016-cycles_denoising
https://developer.blender.org/rBd23f00039f1e953ed5ca0dac3ceccc07230e7170
Cycles: Fix denoising feature matrix norm calculation
===================================================================
M intern/cycles/kernel/kernel_filter.h
===================================================================
diff --git a/intern/cycles/kernel/kernel_filter.h
b/intern/cycles/kernel/kernel_filter.h
index bb9fa26..1dc4387 100644
--- a/intern/cycles/kernel/kernel_filter.h
+++ b/intern/cycles/kernel/kernel_filter.h
@@ -18,6 +18,10 @@
CCL_NAMESPACE_BEGIN
+/* Not all features are included in the matrix norm. */
+#define NORM_FEATURE_OFFSET 2
+#define NORM_FEATURE_NUM 8
+
#define ccl_get_feature(pass) buffer[(pass)*pass_stride]
#define FOR_PIXEL_WINDOW pixel_buffer = buffer + (low.y - rect.y)*buffer_w
+ (low.x - rect.x); \
@@ -304,7 +308,7 @@ 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, FEATURE_PASSES);
+ math_matrix_zero_lower(perturbation_matrix, NORM_FEATURE_NUM);
#endif
FOR_PIXEL_WINDOW {
filter_get_features(px, py, pixel_buffer, features,
feature_means, pass_stride);
@@ -314,10 +318,10 @@ ccl_device void
kernel_filter_estimate_params(KernelGlobals *kg, int sample, flo
filter_get_feature_variance(px, py, pixel_buffer, features,
feature_scale, pass_stride);
#ifdef FULL_EIGENVALUE_NORM
- math_add_gramian(perturbation_matrix, FEATURE_PASSES, features,
kernel_data.integrator.filter_strength);
+ math_add_gramian(perturbation_matrix, NORM_FEATURE_NUM,
features, kernel_data.integrator.filter_strength);
#else
- for(int i = 0; i < FEATURE_PASSES; i++)
- feature_matrix_norm +=
features[i]*kernel_data.integrator.filter_strength;
+ for(int i = 0; i < NORM_FEATURE_NUM; i++)
+ feature_matrix_norm += features[i +
NORM_FEATURE_OFFSET]*kernel_data.integrator.filter_strength;
#endif
} END_FOR_PIXEL_WINDOW
math_lower_tri_to_full(feature_matrix, DENOISE_FEATURES);
@@ -329,7 +333,7 @@ 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, FEATURE_PASSES,
eigenvector_guess, tempvector + 3*DENOISE_FEATURES));
+ float singular_threshold = 0.01f + 2.0f *
sqrtf(math_largest_eigenvalue(perturbation_matrix, NORM_FEATURE_NUM,
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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