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authorStefan Werner <stefan.werner@tangent-animation.com>2020-03-05 14:05:42 +0300
committerStefan Werner <stefan.werner@tangent-animation.com>2020-03-05 14:21:38 +0300
commit51e898324de30c0985a80e5bc067358b5ccedbfc (patch)
tree5efddead1b7ca5655f1d6d2422b59e7da51fe271 /intern/cycles/device/device_optix.cpp
parent4ccbbd308060f0330472828b317c59e054c9ee7b (diff)
Adaptive Sampling for Cycles.
This feature takes some inspiration from "RenderMan: An Advanced Path Tracing Architecture for Movie Rendering" and "A Hierarchical Automatic Stopping Condition for Monte Carlo Global Illumination" The basic principle is as follows: While samples are being added to a pixel, the adaptive sampler writes half of the samples to a separate buffer. This gives it two separate estimates of the same pixel, and by comparing their difference it estimates convergence. Once convergence drops below a given threshold, the pixel is considered done. When a pixel has not converged yet and needs more samples than the minimum, its immediate neighbors are also set to take more samples. This is done in order to more reliably detect sharp features such as caustics. A 3x3 box filter that is run periodically over the tile buffer is used for that purpose. After a tile has finished rendering, the values of all passes are scaled as if they were rendered with the full number of samples. This way, any code operating on these buffers, for example the denoiser, does not need to be changed for per-pixel sample counts. Reviewed By: brecht, #cycles Differential Revision: https://developer.blender.org/D4686
Diffstat (limited to 'intern/cycles/device/device_optix.cpp')
-rw-r--r--intern/cycles/device/device_optix.cpp27
1 files changed, 22 insertions, 5 deletions
diff --git a/intern/cycles/device/device_optix.cpp b/intern/cycles/device/device_optix.cpp
index 61a5c74f69e..30d624fdf7c 100644
--- a/intern/cycles/device/device_optix.cpp
+++ b/intern/cycles/device/device_optix.cpp
@@ -627,7 +627,11 @@ class OptiXDevice : public CUDADevice {
const int end_sample = rtile.start_sample + rtile.num_samples;
// Keep this number reasonable to avoid running into TDRs
- const int step_samples = (info.display_device ? 8 : 32);
+ int step_samples = (info.display_device ? 8 : 32);
+ if (task.adaptive_sampling.use) {
+ step_samples = task.adaptive_sampling.align_static_samples(step_samples);
+ }
+
// Offset into launch params buffer so that streams use separate data
device_ptr launch_params_ptr = launch_params.device_pointer +
thread_index * launch_params.data_elements;
@@ -638,10 +642,9 @@ class OptiXDevice : public CUDADevice {
// Copy work tile information to device
wtile.num_samples = min(step_samples, end_sample - sample);
wtile.start_sample = sample;
- check_result_cuda(cuMemcpyHtoDAsync(launch_params_ptr + offsetof(KernelParams, tile),
- &wtile,
- sizeof(wtile),
- cuda_stream[thread_index]));
+ device_ptr d_wtile_ptr = launch_params_ptr + offsetof(KernelParams, tile);
+ check_result_cuda(
+ cuMemcpyHtoDAsync(d_wtile_ptr, &wtile, sizeof(wtile), cuda_stream[thread_index]));
OptixShaderBindingTable sbt_params = {};
sbt_params.raygenRecord = sbt_data.device_pointer + PG_RGEN * sizeof(SbtRecord);
@@ -666,6 +669,12 @@ class OptiXDevice : public CUDADevice {
wtile.h,
1));
+ // Run the adaptive sampling kernels at selected samples aligned to step samples.
+ uint filter_sample = wtile.start_sample + wtile.num_samples - 1;
+ if (task.adaptive_sampling.use && task.adaptive_sampling.need_filter(filter_sample)) {
+ adaptive_sampling_filter(filter_sample, &wtile, d_wtile_ptr, cuda_stream[thread_index]);
+ }
+
// Wait for launch to finish
check_result_cuda(cuStreamSynchronize(cuda_stream[thread_index]));
@@ -677,6 +686,14 @@ class OptiXDevice : public CUDADevice {
if (task.get_cancel() && !task.need_finish_queue)
return; // Cancel rendering
}
+
+ // Finalize adaptive sampling
+ if (task.adaptive_sampling.use) {
+ device_ptr d_wtile_ptr = launch_params_ptr + offsetof(KernelParams, tile);
+ adaptive_sampling_post(rtile, &wtile, d_wtile_ptr, cuda_stream[thread_index]);
+ check_result_cuda(cuStreamSynchronize(cuda_stream[thread_index]));
+ task.update_progress(&rtile, rtile.w * rtile.h * wtile.num_samples);
+ }
}
bool launch_denoise(DeviceTask &task, RenderTile &rtile)