Files
blender/intern/cycles/kernel/kernel_random.h
Brecht Van Lommel 889d77e6f6 Cycles Volume Render: heterogeneous (textured) volumes support.
Volumes can now have textured colors and density. There is a Volume Sampling
panel in the Render properties with these settings:

* Step size: distance between volume shader samples when rendering the volume.
  Lower values give more accurate and detailed results but also increased render
  time.
* Max steps: maximum number of steps through the volume before giving up, to
  protect from extremely long render times with big objects or small step sizes.

This is much more compute intensive than homogeneous volume, so when you are not
using a texture you should enable the Homogeneous Volume option in the material
or world for faster rendering.

One important missing feature is that Generated texture coordinates are not yet
working in volumes, and they are the default coordinates for nearly all texture
nodes. So until that works you need to plug in object texture coordinates or a
world space position.

This is work by "storm", Stuart Broadfoot, Thomas Dinges and myself.
2013-12-30 00:04:02 +01:00

253 lines
6.5 KiB
C

/*
* Copyright 2011-2013 Blender Foundation
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License
*/
#include "kernel_jitter.h"
CCL_NAMESPACE_BEGIN
#ifdef __SOBOL__
/* skip initial numbers that are not as well distributed, especially the
* first sequence is just 0 everywhere, which can be problematic for e.g.
* path termination */
#define SOBOL_SKIP 64
/* High Dimensional Sobol */
/* van der corput radical inverse */
ccl_device uint van_der_corput(uint bits)
{
bits = (bits << 16) | (bits >> 16);
bits = ((bits & 0x00ff00ff) << 8) | ((bits & 0xff00ff00) >> 8);
bits = ((bits & 0x0f0f0f0f) << 4) | ((bits & 0xf0f0f0f0) >> 4);
bits = ((bits & 0x33333333) << 2) | ((bits & 0xcccccccc) >> 2);
bits = ((bits & 0x55555555) << 1) | ((bits & 0xaaaaaaaa) >> 1);
return bits;
}
/* sobol radical inverse */
ccl_device uint sobol(uint i)
{
uint r = 0;
for(uint v = 1U << 31; i; i >>= 1, v ^= v >> 1)
if(i & 1)
r ^= v;
return r;
}
/* inverse of sobol radical inverse */
ccl_device uint sobol_inverse(uint i)
{
const uint msb = 1U << 31;
uint r = 0;
for(uint v = 1; i; i <<= 1, v ^= v << 1)
if(i & msb)
r ^= v;
return r;
}
/* multidimensional sobol with generator matrices
* dimension 0 and 1 are equal to van_der_corput() and sobol() respectively */
ccl_device uint sobol_dimension(KernelGlobals *kg, int index, int dimension)
{
uint result = 0;
uint i = index;
for(uint j = 0; i; i >>= 1, j++)
if(i & 1)
result ^= kernel_tex_fetch(__sobol_directions, 32*dimension + j);
return result;
}
/* lookup index and x/y coordinate, assumes m is a power of two */
ccl_device uint sobol_lookup(const uint m, const uint frame, const uint ex, const uint ey, uint *x, uint *y)
{
/* shift is constant per frame */
const uint shift = frame << (m << 1);
const uint sobol_shift = sobol(shift);
/* van der Corput is its own inverse */
const uint lower = van_der_corput(ex << (32 - m));
/* need to compensate for ey difference and shift */
const uint sobol_lower = sobol(lower);
const uint mask = ~-(1 << m) << (32 - m); /* only m upper bits */
const uint delta = ((ey << (32 - m)) ^ sobol_lower ^ sobol_shift) & mask;
/* only use m upper bits for the index (m is a power of two) */
const uint sobol_result = delta | (delta >> m);
const uint upper = sobol_inverse(sobol_result);
const uint index = shift | upper | lower;
*x = van_der_corput(index);
*y = sobol_shift ^ sobol_result ^ sobol_lower;
return index;
}
ccl_device_inline float path_rng_1D(KernelGlobals *kg, RNG *rng, int sample, int num_samples, int dimension)
{
#ifdef __CMJ__
if(kernel_data.integrator.sampling_pattern == SAMPLING_PATTERN_CMJ) {
/* correlated multi-jittered */
int p = *rng + dimension;
return cmj_sample_1D(sample, num_samples, p);
}
#endif
#ifdef __SOBOL_FULL_SCREEN__
uint result = sobol_dimension(kg, *rng, dimension);
float r = (float)result * (1.0f/(float)0xFFFFFFFF);
return r;
#else
/* compute sobol sequence value using direction vectors */
uint result = sobol_dimension(kg, sample + SOBOL_SKIP, dimension);
float r = (float)result * (1.0f/(float)0xFFFFFFFF);
/* Cranly-Patterson rotation using rng seed */
float shift;
if(dimension & 1)
shift = (*rng >> 16) * (1.0f/(float)0xFFFF);
else
shift = (*rng & 0xFFFF) * (1.0f/(float)0xFFFF);
return r + shift - floorf(r + shift);
#endif
}
ccl_device_inline void path_rng_2D(KernelGlobals *kg, RNG *rng, int sample, int num_samples, int dimension, float *fx, float *fy)
{
#ifdef __CMJ__
if(kernel_data.integrator.sampling_pattern == SAMPLING_PATTERN_CMJ) {
/* correlated multi-jittered */
int p = *rng + dimension;
cmj_sample_2D(sample, num_samples, p, fx, fy);
}
else
#endif
{
/* sobol */
*fx = path_rng_1D(kg, rng, sample, num_samples, dimension);
*fy = path_rng_1D(kg, rng, sample, num_samples, dimension + 1);
}
}
ccl_device_inline void path_rng_init(KernelGlobals *kg, ccl_global uint *rng_state, int sample, int num_samples, RNG *rng, int x, int y, float *fx, float *fy)
{
#ifdef __SOBOL_FULL_SCREEN__
uint px, py;
uint bits = 16; /* limits us to 65536x65536 and 65536 samples */
uint size = 1 << bits;
uint frame = sample;
*rng = sobol_lookup(bits, frame, x, y, &px, &py);
*rng ^= kernel_data.integrator.seed;
if(sample == 0) {
*fx = 0.5f;
*fy = 0.5f;
}
else {
*fx = size * (float)px * (1.0f/(float)0xFFFFFFFF) - x;
*fy = size * (float)py * (1.0f/(float)0xFFFFFFFF) - y;
}
#else
*rng = *rng_state;
*rng ^= kernel_data.integrator.seed;
if(sample == 0) {
*fx = 0.5f;
*fy = 0.5f;
}
else {
path_rng_2D(kg, rng, sample, num_samples, PRNG_FILTER_U, fx, fy);
}
#endif
}
ccl_device void path_rng_end(KernelGlobals *kg, ccl_global uint *rng_state, RNG rng)
{
/* nothing to do */
}
#else
/* Linear Congruential Generator */
ccl_device_inline float path_rng_1D(KernelGlobals *kg, RNG& rng, int sample, int num_samples, int dimension)
{
/* implicit mod 2^32 */
rng = (1103515245*(rng) + 12345);
return (float)rng * (1.0f/(float)0xFFFFFFFF);
}
ccl_device_inline void path_rng_2D(KernelGlobals *kg, RNG& rng, int sample, int num_samples, int dimension, float *fx, float *fy)
{
*fx = path_rng_1D(kg, rng, sample, num_samples, dimension);
*fy = path_rng_1D(kg, rng, sample, num_samples, dimension + 1);
}
ccl_device void path_rng_init(KernelGlobals *kg, ccl_global uint *rng_state, int sample, int num_samples, RNG *rng, int x, int y, float *fx, float *fy)
{
/* load state */
*rng = *rng_state;
*rng ^= kernel_data.integrator.seed;
if(sample == 0) {
*fx = 0.5f;
*fy = 0.5f;
}
else {
path_rng_2D(kg, rng, sample, num_samples, PRNG_FILTER_U, fx, fy);
}
}
ccl_device void path_rng_end(KernelGlobals *kg, ccl_global uint *rng_state, RNG rng)
{
/* store state for next sample */
*rng_state = rng;
}
#endif
ccl_device uint lcg_step_uint(uint *rng)
{
/* implicit mod 2^32 */
*rng = (1103515245*(*rng) + 12345);
return *rng;
}
ccl_device float lcg_step_float(uint *rng)
{
/* implicit mod 2^32 */
*rng = (1103515245*(*rng) + 12345);
return (float)*rng * (1.0f/(float)0xFFFFFFFF);
}
ccl_device uint lcg_init(uint seed)
{
uint rng = seed;
lcg_step_uint(&rng);
return rng;
}
CCL_NAMESPACE_END