This patch implements blue-noise dithered sampling as described by Nathan Vegdahl (https://psychopath.io/post/2022_07_24_owen_scrambling_based_dithered_blue_noise_sampling), which in turn is based on "Screen-Space Blue-Noise Diffusion of Monte Carlo Sampling Error via Hierarchical Ordering of Pixels"(https://repository.kaust.edu.sa/items/1269ae24-2596-400b-a839-e54486033a93). The basic idea is simple: Instead of generating independent sequences for each pixel by scrambling them, we use a single sequence for the entire image, with each pixel getting one chunk of the samples. The ordering across pixels is determined by hierarchical scrambling of the pixel's position along a space-filling curve, which ends up being pretty much the same operation as already used for the underlying sequence. This results in a more high-frequency noise distribution, which appears smoother despite not being less noisy overall. The main limitation at the moment is that the improvement is only clear if the full sample amount is used per pixel, so interactive preview rendering and adaptive sampling will not receive the benefit. One exception to this is that when using the new "Automatic" setting, the first sample in interactive rendering will also be blue-noise-distributed. The sampling mode option is now exposed in the UI, with the three options being Blue Noise (the new mode), Classic (the previous Tabulated Sobol method) and the new default, Automatic (blue noise, with the additional property of ensuring the first sample is also blue-noise-distributed in interactive rendering). When debug mode is enabled, additional options appear, such as Sobol-Burley. Note that the scrambling distance option is not compatible with the blue-noise pattern. Pull Request: https://projects.blender.org/blender/blender/pulls/118479
202 lines
6.5 KiB
C
202 lines
6.5 KiB
C
/* SPDX-FileCopyrightText: 2011-2022 Blender Foundation
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*
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* SPDX-License-Identifier: Apache-2.0 */
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#include "kernel/integrator/guiding.h"
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CCL_NAMESPACE_BEGIN
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/* BSSRDF using disk based importance sampling.
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*
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* BSSRDF Importance Sampling, SIGGRAPH 2013
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* http://library.imageworks.com/pdfs/imageworks-library-BSSRDF-sampling.pdf
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*/
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ccl_device_inline Spectrum subsurface_disk_eval(const Spectrum radius, float disk_r, float r)
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{
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const Spectrum eval = bssrdf_eval(radius, r);
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const float pdf = bssrdf_pdf(radius, disk_r);
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return (pdf > 0.0f) ? eval / pdf : zero_spectrum();
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}
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/* Subsurface scattering step, from a point on the surface to other
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* nearby points on the same object. */
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ccl_device_inline bool subsurface_disk(KernelGlobals kg,
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IntegratorState state,
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RNGState rng_state,
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ccl_private Ray &ray,
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ccl_private LocalIntersection &ss_isect)
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{
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float2 rand_disk = path_state_rng_2D(kg, &rng_state, PRNG_SUBSURFACE_DISK);
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/* Read shading point info from integrator state. */
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const float3 P = INTEGRATOR_STATE(state, ray, P);
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const float ray_dP = INTEGRATOR_STATE(state, ray, dP);
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const float time = INTEGRATOR_STATE(state, ray, time);
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const float3 Ng = INTEGRATOR_STATE(state, subsurface, N);
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const int object = INTEGRATOR_STATE(state, isect, object);
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const uint32_t path_flag = INTEGRATOR_STATE(state, path, flag);
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/* Read subsurface scattering parameters. */
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const Spectrum radius = INTEGRATOR_STATE(state, subsurface, radius);
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/* Pick random axis in local frame and point on disk. */
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float3 disk_N, disk_T, disk_B;
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float pick_pdf_N, pick_pdf_T, pick_pdf_B;
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disk_N = Ng;
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make_orthonormals(disk_N, &disk_T, &disk_B);
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if (rand_disk.y < 0.5f) {
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pick_pdf_N = 0.5f;
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pick_pdf_T = 0.25f;
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pick_pdf_B = 0.25f;
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rand_disk.y *= 2.0f;
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}
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else if (rand_disk.y < 0.75f) {
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float3 tmp = disk_N;
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disk_N = disk_T;
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disk_T = tmp;
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pick_pdf_N = 0.25f;
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pick_pdf_T = 0.5f;
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pick_pdf_B = 0.25f;
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rand_disk.y = (rand_disk.y - 0.5f) * 4.0f;
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}
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else {
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float3 tmp = disk_N;
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disk_N = disk_B;
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disk_B = tmp;
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pick_pdf_N = 0.25f;
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pick_pdf_T = 0.25f;
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pick_pdf_B = 0.5f;
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rand_disk.y = (rand_disk.y - 0.75f) * 4.0f;
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}
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/* Sample point on disk. */
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float phi = M_2PI_F * rand_disk.y;
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float disk_height, disk_r;
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bssrdf_sample(radius, rand_disk.x, &disk_r, &disk_height);
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float3 disk_P = (disk_r * cosf(phi)) * disk_T + (disk_r * sinf(phi)) * disk_B;
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/* Create ray. */
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ray.P = P + disk_N * disk_height + disk_P;
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ray.D = -disk_N;
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ray.tmin = 0.0f;
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ray.tmax = 2.0f * disk_height;
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ray.dP = ray_dP;
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ray.dD = differential_zero_compact();
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ray.time = time;
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ray.self.object = OBJECT_NONE;
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ray.self.prim = PRIM_NONE;
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ray.self.light_object = OBJECT_NONE;
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ray.self.light_prim = PRIM_NONE;
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ray.self.light = LAMP_NONE;
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/* Intersect with the same object. if multiple intersections are found it
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* will use at most BSSRDF_MAX_HITS hits, a random subset of all hits. */
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uint lcg_state = lcg_state_init(
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rng_state.rng_pixel, rng_state.rng_offset, rng_state.sample, 0x68bc21eb);
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const int max_hits = BSSRDF_MAX_HITS;
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scene_intersect_local(kg, &ray, &ss_isect, object, &lcg_state, max_hits);
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const int num_eval_hits = min(ss_isect.num_hits, max_hits);
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if (num_eval_hits == 0) {
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return false;
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}
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/* Sort for consistent renders between CPU and GPU, independent of the BVH
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* traversal algorithm. */
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sort_intersections_and_normals(ss_isect.hits, ss_isect.Ng, num_eval_hits);
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Spectrum weights[BSSRDF_MAX_HITS]; /* TODO: zero? */
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float sum_weights = 0.0f;
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for (int hit = 0; hit < num_eval_hits; hit++) {
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/* Get geometric normal. */
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const int object = ss_isect.hits[hit].object;
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const int object_flag = kernel_data_fetch(object_flag, object);
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float3 hit_Ng = ss_isect.Ng[hit];
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if (path_flag & PATH_RAY_SUBSURFACE_BACKFACING) {
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hit_Ng = -hit_Ng;
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}
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if (object_negative_scale_applied(object_flag)) {
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hit_Ng = -hit_Ng;
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}
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if (!(object_flag & SD_OBJECT_TRANSFORM_APPLIED)) {
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/* Transform normal to world space. */
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Transform itfm;
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object_fetch_transform_motion_test(kg, object, time, &itfm);
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hit_Ng = normalize(transform_direction_transposed(&itfm, hit_Ng));
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}
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/* Quickly retrieve P and Ng without setting up ShaderData. */
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const float3 hit_P = ray.P + ray.D * ss_isect.hits[hit].t;
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/* Probability densities for local frame axes. */
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const float pdf_N = pick_pdf_N * fabsf(dot(disk_N, hit_Ng));
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const float pdf_T = pick_pdf_T * fabsf(dot(disk_T, hit_Ng));
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const float pdf_B = pick_pdf_B * fabsf(dot(disk_B, hit_Ng));
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/* Multiple importance sample between 3 axes, power heuristic
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* found to be slightly better than balance heuristic. pdf_N
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* in the MIS weight and denominator cancelled out. */
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float w = pdf_N / (sqr(pdf_N) + sqr(pdf_T) + sqr(pdf_B));
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if (ss_isect.num_hits > max_hits) {
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w *= ss_isect.num_hits / (float)max_hits;
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}
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/* Real distance to sampled point. */
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const float r = len(hit_P - P);
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/* Evaluate profiles. */
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const Spectrum weight = subsurface_disk_eval(radius, disk_r, r) * w;
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/* Store result. */
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ss_isect.Ng[hit] = hit_Ng;
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weights[hit] = weight;
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sum_weights += average(fabs(weight));
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}
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if (sum_weights == 0.0f) {
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return false;
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}
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/* Use importance resampling, sampling one of the hits proportional to weight. */
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const float rand_resample = path_state_rng_1D(kg, &rng_state, PRNG_SUBSURFACE_DISK_RESAMPLE);
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const float r = rand_resample * sum_weights;
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float partial_sum = 0.0f;
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for (int hit = 0; hit < num_eval_hits; hit++) {
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const Spectrum weight = weights[hit];
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const float sample_weight = average(fabs(weight));
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float next_sum = partial_sum + sample_weight;
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if (r < next_sum) {
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/* Return exit point. */
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const Spectrum resampled_weight = weight * sum_weights / sample_weight;
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INTEGRATOR_STATE_WRITE(state, path, throughput) *= resampled_weight;
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ss_isect.hits[0] = ss_isect.hits[hit];
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ss_isect.Ng[0] = ss_isect.Ng[hit];
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ray.P = ray.P + ray.D * ss_isect.hits[hit].t;
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ray.D = ss_isect.Ng[hit];
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ray.tmin = 0.0f;
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ray.tmax = 1.0f;
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guiding_record_bssrdf_bounce(
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kg, state, 1.0f, Ng, -Ng, resampled_weight, INTEGRATOR_STATE(state, subsurface, albedo));
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return true;
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}
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partial_sum = next_sum;
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}
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return false;
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}
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CCL_NAMESPACE_END
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