Cycles: remove old Sobol pattern, simplify sampling dimensions

The multi-dimensional Sobol pattern required us to carefully use as low
dimensions as possible, as quality goes down in higher dimensions. Now that we
have two sampling patterns that are at least as good, there is no need to keep
it around and the implementation can be simplified.

Differential Revision: https://developer.blender.org/D15788

1 files changed, 3 insertions, 64 deletions

diff --git a/intern/cycles/kernel/sample/pattern.h b/intern/cycles/kernel/sample/pattern.h
index e8c3acb5cf7..cc5d0e960ec 100644
--- a/intern/cycles/kernel/sample/pattern.h
+++ b/intern/cycles/kernel/sample/pattern.h
@@ -13,33 +13,6 @@ CCL_NAMESPACE_BEGIN
  * this single threaded on a CPU for repeatable results. */
 //#define __DEBUG_CORRELATION__
 
-/* High Dimensional Sobol.
- *
- * Multidimensional sobol with generator matrices. Dimension 0 and 1 are equal
- * to classic Van der Corput and Sobol sequences. */
-
-#ifdef __SOBOL__
-
-/* Skip initial numbers that for some dimensions have clear patterns that
- * don't cover the entire sample space. Ideally we would have a better
- * progressive pattern that doesn't suffer from this problem, because even
- * with this offset some dimensions are quite poor.
- */
-#  define SOBOL_SKIP 64
-
-ccl_device uint sobol_dimension(KernelGlobals kg, int index, int dimension)
-{
-  uint result = 0;
-  uint i = index + SOBOL_SKIP;
-  for (int j = 0, x; (x = find_first_set(i)); i >>= x) {
-    j += x;
-    result ^= __float_as_uint(kernel_data_fetch(sample_pattern_lut, 32 * dimension + j - 1));
-  }
-  return result;
-}
-
-#endif /* __SOBOL__ */
-
 ccl_device_forceinline float path_rng_1D(KernelGlobals kg,
                                          uint rng_hash,
                                          int sample,
@@ -52,30 +25,9 @@ ccl_device_forceinline float path_rng_1D(KernelGlobals kg,
   if (kernel_data.integrator.sampling_pattern == SAMPLING_PATTERN_SOBOL_BURLEY) {
     return sobol_burley_sample_1D(sample, dimension, rng_hash);
   }
-
-#ifdef __SOBOL__
-  if (kernel_data.integrator.sampling_pattern == SAMPLING_PATTERN_PMJ)
-#endif
-  {
+  else {
     return pmj_sample_1D(kg, sample, rng_hash, dimension);
   }
-
-#ifdef __SOBOL__
-  /* Sobol sequence value using direction vectors. */
-  uint result = sobol_dimension(kg, sample, dimension);
-  float r = (float)result * (1.0f / (float)0xFFFFFFFF);
-
-  /* Cranly-Patterson rotation using rng seed */
-  float shift;
-
-  /* Hash rng with dimension to solve correlation issues.
-   * See T38710, T50116.
-   */
-  uint tmp_rng = hash_wang_seeded_uint(dimension, rng_hash);
-  shift = tmp_rng * (kernel_data.integrator.scrambling_distance / (float)0xFFFFFFFF);
-
-  return r + shift - floorf(r + shift);
-#endif
 }
 
 ccl_device_forceinline void path_rng_2D(KernelGlobals kg,
@@ -93,23 +45,10 @@ ccl_device_forceinline void path_rng_2D(KernelGlobals kg,
 
   if (kernel_data.integrator.sampling_pattern == SAMPLING_PATTERN_SOBOL_BURLEY) {
     sobol_burley_sample_2D(sample, dimension, rng_hash, fx, fy);
-    return;
   }
-
-#ifdef __SOBOL__
-  if (kernel_data.integrator.sampling_pattern == SAMPLING_PATTERN_PMJ)
-#endif
-  {
+  else {
     pmj_sample_2D(kg, sample, rng_hash, dimension, fx, fy);
-
-    return;
   }
-
-#ifdef __SOBOL__
-  /* Sobol. */
-  *fx = path_rng_1D(kg, rng_hash, sample, dimension);
-  *fy = path_rng_1D(kg, rng_hash, sample, dimension + 1);
-#endif
 }
 
 /**
@@ -164,7 +103,7 @@ ccl_device_inline bool sample_is_even(int pattern, int sample)
     return popcount(uint(sample) & 0xaaaaaaaa) & 1;
   }
   else {
-    /* TODO(Stefan): Are there reliable ways of dividing CMJ and Sobol into two classes? */
+    /* TODO(Stefan): Are there reliable ways of dividing Sobol-Burley into two classes? */
     return sample & 0x1;
   }
 }