cc600de669
Previously, bright edges (e.g. caused by rim lighting) would sometimes get halos around them after denoising. This change introduces a log(1+x) highlight compression step that is performed before denoising and reversed afterwards. That way, the denoising algorithm itself operates in the compressed space and therefore bright edges cause less numerical issues.
354 lines
13 KiB
C++
354 lines
13 KiB
C++
/*
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* Copyright 2011-2017 Blender Foundation
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "device/device_denoising.h"
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#include "kernel/filter/filter_defines.h"
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CCL_NAMESPACE_BEGIN
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DenoisingTask::DenoisingTask(Device *device, const DeviceTask &task)
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: tile_info_mem(device, "denoising tile info mem", MEM_READ_WRITE),
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profiler(NULL),
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storage(device),
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buffer(device),
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device(device)
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{
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radius = task.denoising.radius;
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nlm_k_2 = powf(2.0f, lerp(-5.0f, 3.0f, task.denoising.strength));
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if (task.denoising.relative_pca) {
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pca_threshold = -powf(10.0f, lerp(-8.0f, 0.0f, task.denoising.feature_strength));
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}
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else {
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pca_threshold = powf(10.0f, lerp(-5.0f, 3.0f, task.denoising.feature_strength));
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}
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render_buffer.frame_stride = task.frame_stride;
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render_buffer.pass_stride = task.pass_stride;
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render_buffer.offset = task.pass_denoising_data;
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target_buffer.pass_stride = task.target_pass_stride;
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target_buffer.denoising_clean_offset = task.pass_denoising_clean;
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target_buffer.offset = 0;
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functions.map_neighbor_tiles = function_bind(task.map_neighbor_tiles, _1, device);
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functions.unmap_neighbor_tiles = function_bind(task.unmap_neighbor_tiles, _1, device);
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tile_info = (TileInfo *)tile_info_mem.alloc(sizeof(TileInfo) / sizeof(int));
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tile_info->from_render = task.denoising_from_render ? 1 : 0;
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tile_info->frames[0] = 0;
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tile_info->num_frames = min(task.denoising_frames.size() + 1, DENOISE_MAX_FRAMES);
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for (int i = 1; i < tile_info->num_frames; i++) {
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tile_info->frames[i] = task.denoising_frames[i - 1];
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}
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write_passes = task.denoising_write_passes;
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do_filter = task.denoising_do_filter;
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}
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DenoisingTask::~DenoisingTask()
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{
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storage.XtWX.free();
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storage.XtWY.free();
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storage.transform.free();
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storage.rank.free();
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buffer.mem.free();
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buffer.temporary_mem.free();
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tile_info_mem.free();
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}
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void DenoisingTask::set_render_buffer(RenderTile *rtiles)
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{
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for (int i = 0; i < 9; i++) {
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tile_info->offsets[i] = rtiles[i].offset;
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tile_info->strides[i] = rtiles[i].stride;
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tile_info->buffers[i] = rtiles[i].buffer;
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}
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tile_info->x[0] = rtiles[3].x;
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tile_info->x[1] = rtiles[4].x;
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tile_info->x[2] = rtiles[5].x;
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tile_info->x[3] = rtiles[5].x + rtiles[5].w;
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tile_info->y[0] = rtiles[1].y;
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tile_info->y[1] = rtiles[4].y;
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tile_info->y[2] = rtiles[7].y;
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tile_info->y[3] = rtiles[7].y + rtiles[7].h;
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target_buffer.offset = rtiles[9].offset;
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target_buffer.stride = rtiles[9].stride;
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target_buffer.ptr = rtiles[9].buffer;
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if (write_passes && rtiles[9].buffers) {
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target_buffer.denoising_output_offset =
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rtiles[9].buffers->params.get_denoising_prefiltered_offset();
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}
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else {
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target_buffer.denoising_output_offset = 0;
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}
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tile_info_mem.copy_to_device();
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}
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void DenoisingTask::setup_denoising_buffer()
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{
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/* Expand filter_area by radius pixels and clamp the result to the extent of the neighboring
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* tiles */
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rect = rect_from_shape(filter_area.x, filter_area.y, filter_area.z, filter_area.w);
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rect = rect_expand(rect, radius);
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rect = rect_clip(rect,
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make_int4(tile_info->x[0], tile_info->y[0], tile_info->x[3], tile_info->y[3]));
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buffer.use_intensity = write_passes || (tile_info->num_frames > 1);
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buffer.passes = buffer.use_intensity ? 15 : 14;
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buffer.width = rect.z - rect.x;
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buffer.stride = align_up(buffer.width, 4);
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buffer.h = rect.w - rect.y;
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int alignment_floats = divide_up(device->mem_sub_ptr_alignment(), sizeof(float));
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buffer.pass_stride = align_up(buffer.stride * buffer.h, alignment_floats);
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buffer.frame_stride = buffer.pass_stride * buffer.passes;
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/* Pad the total size by four floats since the SIMD kernels might go a bit over the end. */
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int mem_size = align_up(tile_info->num_frames * buffer.frame_stride + 4, alignment_floats);
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buffer.mem.alloc_to_device(mem_size, false);
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buffer.use_time = (tile_info->num_frames > 1);
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/* CPUs process shifts sequentially while GPUs process them in parallel. */
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int num_layers;
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if (buffer.gpu_temporary_mem) {
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/* Shadowing prefiltering uses a radius of 6, so allocate at least that much. */
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int max_radius = max(radius, 6);
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int num_shifts = (2 * max_radius + 1) * (2 * max_radius + 1);
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num_layers = 2 * num_shifts + 1;
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}
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else {
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num_layers = 3;
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}
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/* Allocate two layers per shift as well as one for the weight accumulation. */
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buffer.temporary_mem.alloc_to_device(num_layers * buffer.pass_stride);
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}
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void DenoisingTask::prefilter_shadowing()
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{
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device_ptr null_ptr = (device_ptr)0;
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device_sub_ptr unfiltered_a(buffer.mem, 0, buffer.pass_stride);
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device_sub_ptr unfiltered_b(buffer.mem, 1 * buffer.pass_stride, buffer.pass_stride);
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device_sub_ptr sample_var(buffer.mem, 2 * buffer.pass_stride, buffer.pass_stride);
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device_sub_ptr sample_var_var(buffer.mem, 3 * buffer.pass_stride, buffer.pass_stride);
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device_sub_ptr buffer_var(buffer.mem, 5 * buffer.pass_stride, buffer.pass_stride);
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device_sub_ptr filtered_var(buffer.mem, 6 * buffer.pass_stride, buffer.pass_stride);
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/* Get the A/B unfiltered passes, the combined sample variance, the estimated variance of the
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* sample variance and the buffer variance. */
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functions.divide_shadow(*unfiltered_a, *unfiltered_b, *sample_var, *sample_var_var, *buffer_var);
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/* Smooth the (generally pretty noisy) buffer variance using the spatial information from the
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* sample variance. */
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nlm_state.set_parameters(6, 3, 4.0f, 1.0f, false);
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functions.non_local_means(*buffer_var, *sample_var, *sample_var_var, *filtered_var);
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/* Reuse memory, the previous data isn't needed anymore. */
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device_ptr filtered_a = *buffer_var, filtered_b = *sample_var;
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/* Use the smoothed variance to filter the two shadow half images using each other for weight
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* calculation. */
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nlm_state.set_parameters(5, 3, 1.0f, 0.25f, false);
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functions.non_local_means(*unfiltered_a, *unfiltered_b, *filtered_var, filtered_a);
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functions.non_local_means(*unfiltered_b, *unfiltered_a, *filtered_var, filtered_b);
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device_ptr residual_var = *sample_var_var;
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/* Estimate the residual variance between the two filtered halves. */
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functions.combine_halves(filtered_a, filtered_b, null_ptr, residual_var, 2, rect);
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device_ptr final_a = *unfiltered_a, final_b = *unfiltered_b;
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/* Use the residual variance for a second filter pass. */
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nlm_state.set_parameters(4, 2, 1.0f, 0.5f, false);
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functions.non_local_means(filtered_a, filtered_b, residual_var, final_a);
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functions.non_local_means(filtered_b, filtered_a, residual_var, final_b);
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/* Combine the two double-filtered halves to a final shadow feature. */
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device_sub_ptr shadow_pass(buffer.mem, 4 * buffer.pass_stride, buffer.pass_stride);
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functions.combine_halves(final_a, final_b, *shadow_pass, null_ptr, 0, rect);
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}
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void DenoisingTask::prefilter_features()
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{
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device_sub_ptr unfiltered(buffer.mem, 8 * buffer.pass_stride, buffer.pass_stride);
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device_sub_ptr variance(buffer.mem, 9 * buffer.pass_stride, buffer.pass_stride);
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int mean_from[] = {0, 1, 2, 12, 6, 7, 8};
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int variance_from[] = {3, 4, 5, 13, 9, 10, 11};
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int pass_to[] = {1, 2, 3, 0, 5, 6, 7};
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for (int pass = 0; pass < 7; pass++) {
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device_sub_ptr feature_pass(
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buffer.mem, pass_to[pass] * buffer.pass_stride, buffer.pass_stride);
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/* Get the unfiltered pass and its variance from the RenderBuffers. */
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functions.get_feature(mean_from[pass],
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variance_from[pass],
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*unfiltered,
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*variance,
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1.0f / render_buffer.samples);
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/* Smooth the pass and store the result in the denoising buffers. */
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nlm_state.set_parameters(2, 2, 1.0f, 0.25f, false);
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functions.non_local_means(*unfiltered, *unfiltered, *variance, *feature_pass);
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}
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}
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void DenoisingTask::prefilter_color()
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{
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int mean_from[] = {20, 21, 22};
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int variance_from[] = {23, 24, 25};
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int mean_to[] = {8, 9, 10};
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int variance_to[] = {11, 12, 13};
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int num_color_passes = 3;
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device_only_memory<float> temporary_color(device, "denoising temporary color");
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temporary_color.alloc_to_device(6 * buffer.pass_stride, false);
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for (int pass = 0; pass < num_color_passes; pass++) {
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device_sub_ptr color_pass(temporary_color, pass * buffer.pass_stride, buffer.pass_stride);
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device_sub_ptr color_var_pass(
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temporary_color, (pass + 3) * buffer.pass_stride, buffer.pass_stride);
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functions.get_feature(mean_from[pass],
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variance_from[pass],
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*color_pass,
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*color_var_pass,
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1.0f / render_buffer.samples);
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}
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device_sub_ptr depth_pass(buffer.mem, 0, buffer.pass_stride);
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device_sub_ptr color_var_pass(
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buffer.mem, variance_to[0] * buffer.pass_stride, 3 * buffer.pass_stride);
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device_sub_ptr output_pass(buffer.mem, mean_to[0] * buffer.pass_stride, 3 * buffer.pass_stride);
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functions.detect_outliers(
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temporary_color.device_pointer, *color_var_pass, *depth_pass, *output_pass);
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if (buffer.use_intensity) {
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device_sub_ptr intensity_pass(buffer.mem, 14 * buffer.pass_stride, buffer.pass_stride);
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nlm_state.set_parameters(radius, 4, 2.0f, nlm_k_2 * 4.0f, true);
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functions.non_local_means(*output_pass, *output_pass, *color_var_pass, *intensity_pass);
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}
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}
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void DenoisingTask::load_buffer()
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{
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device_ptr null_ptr = (device_ptr)0;
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int original_offset = render_buffer.offset;
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int num_passes = buffer.use_intensity ? 15 : 14;
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for (int i = 0; i < tile_info->num_frames; i++) {
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for (int pass = 0; pass < num_passes; pass++) {
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device_sub_ptr to_pass(
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buffer.mem, i * buffer.frame_stride + pass * buffer.pass_stride, buffer.pass_stride);
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bool is_variance = (pass >= 11) && (pass <= 13);
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functions.get_feature(
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pass, -1, *to_pass, null_ptr, is_variance ? (1.0f / render_buffer.samples) : 1.0f);
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}
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render_buffer.offset += render_buffer.frame_stride;
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}
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render_buffer.offset = original_offset;
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}
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void DenoisingTask::write_buffer()
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{
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reconstruction_state.buffer_params = make_int4(target_buffer.offset,
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target_buffer.stride,
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target_buffer.pass_stride,
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target_buffer.denoising_clean_offset);
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int num_passes = buffer.use_intensity ? 15 : 14;
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for (int pass = 0; pass < num_passes; pass++) {
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device_sub_ptr from_pass(buffer.mem, pass * buffer.pass_stride, buffer.pass_stride);
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int out_offset = pass + target_buffer.denoising_output_offset;
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functions.write_feature(out_offset, *from_pass, target_buffer.ptr);
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}
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}
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void DenoisingTask::construct_transform()
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{
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storage.w = filter_area.z;
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storage.h = filter_area.w;
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storage.transform.alloc_to_device(storage.w * storage.h * TRANSFORM_SIZE, false);
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storage.rank.alloc_to_device(storage.w * storage.h, false);
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functions.construct_transform();
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}
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void DenoisingTask::reconstruct()
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{
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storage.XtWX.alloc_to_device(storage.w * storage.h * XTWX_SIZE, false);
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storage.XtWY.alloc_to_device(storage.w * storage.h * XTWY_SIZE, false);
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storage.XtWX.zero_to_device();
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storage.XtWY.zero_to_device();
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reconstruction_state.filter_window = rect_from_shape(
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filter_area.x - rect.x, filter_area.y - rect.y, storage.w, storage.h);
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int tile_coordinate_offset = filter_area.y * target_buffer.stride + filter_area.x;
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reconstruction_state.buffer_params = make_int4(target_buffer.offset + tile_coordinate_offset,
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target_buffer.stride,
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target_buffer.pass_stride,
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target_buffer.denoising_clean_offset);
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reconstruction_state.source_w = rect.z - rect.x;
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reconstruction_state.source_h = rect.w - rect.y;
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device_sub_ptr color_ptr(buffer.mem, 8 * buffer.pass_stride, 3 * buffer.pass_stride);
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device_sub_ptr color_var_ptr(buffer.mem, 11 * buffer.pass_stride, 3 * buffer.pass_stride);
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for (int f = 0; f < tile_info->num_frames; f++) {
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device_ptr scale_ptr = 0;
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device_sub_ptr *scale_sub_ptr = NULL;
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if (tile_info->frames[f] != 0 && (tile_info->num_frames > 1)) {
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scale_sub_ptr = new device_sub_ptr(buffer.mem, 14 * buffer.pass_stride, buffer.pass_stride);
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scale_ptr = **scale_sub_ptr;
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}
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functions.accumulate(*color_ptr, *color_var_ptr, scale_ptr, f);
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delete scale_sub_ptr;
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}
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functions.solve(target_buffer.ptr);
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}
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void DenoisingTask::run_denoising(RenderTile *tile)
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{
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RenderTile rtiles[10];
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rtiles[4] = *tile;
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functions.map_neighbor_tiles(rtiles);
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set_render_buffer(rtiles);
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setup_denoising_buffer();
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if (tile_info->from_render) {
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prefilter_shadowing();
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prefilter_features();
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prefilter_color();
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}
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else {
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load_buffer();
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}
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if (do_filter) {
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construct_transform();
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reconstruct();
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}
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if (write_passes) {
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write_buffer();
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}
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functions.unmap_neighbor_tiles(rtiles);
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}
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CCL_NAMESPACE_END
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