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RWMC fix #949

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Merged
merged 1 commit into from
Nov 19, 2025
Merged

RWMC fix #949

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2 changes: 1 addition & 1 deletion examples_tests
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Submodule examples_tests updated 1 files
+5 −5 31_HLSLPathTracer/main.cpp
60 changes: 31 additions & 29 deletions include/nbl/builtin/hlsl/rwmc/resolve.hlsl
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,8 @@
#include <nbl/builtin/hlsl/colorspace/encodeCIEXYZ.hlsl>
#include <nbl/builtin/hlsl/rwmc/ResolveParameters.hlsl>
#include <nbl/builtin/hlsl/concepts/accessors/loadable_image.hlsl>
#include <nbl/builtin/hlsl/colorspace.hlsl>
#include <nbl/builtin/hlsl/vector_utils/vector_traits.hlsl>

namespace nbl
{
Expand All @@ -19,23 +21,21 @@ namespace rwmc
// not the greatest syntax but works
#define NBL_CONCEPT_PARAM_0 (a,T)
#define NBL_CONCEPT_PARAM_1 (scalar,VectorScalarType)
#define NBL_CONCEPT_PARAM_2 (vec,vector<VectorScalarType, Dims>)
// start concept
NBL_CONCEPT_BEGIN(2)
// need to be defined AFTER the concept begins
#define a NBL_CONCEPT_PARAM_T NBL_CONCEPT_PARAM_0
#define scalar NBL_CONCEPT_PARAM_T NBL_CONCEPT_PARAM_1
#define vec NBL_CONCEPT_PARAM_T NBL_CONCEPT_PARAM_2
NBL_CONCEPT_END(
((NBL_CONCEPT_REQ_EXPR)((a.calcLuma(vec))))
((NBL_CONCEPT_REQ_EXPR)((a.calcLuma(vector<VectorScalarType, 3>(scalar, scalar, scalar)))))
);
#undef a
#undef vec
#undef scalar
#include <nbl/builtin/hlsl/concepts/__end.hlsl>

/* ResolveAccessor is required to:
* - satisfy `LoadableImage` concept requirements
* - implement function called `calcLuma` which calculates luma from a pixel value
* - implement function called `calcLuma` which calculates luma from a 3 component pixel value
*/

template<typename T, typename VectorScalarType, int32_t Dims>
Expand All @@ -50,9 +50,9 @@ struct ResolveAccessorAdaptor

RWTexture2DArray<float32_t4> cascade;

float32_t calcLuma(in float32_t3 col)
float32_t calcLuma(NBL_REF_ARG(float32_t3) col)
{
return hlsl::dot<float32_t3>(hlsl::transpose(colorspace::scRGBtoXYZ)[1], col);
return hlsl::dot<float32_t3>(colorspace::scRGB::ToXYZ()[1], col);
}

template<typename OutputScalarType, int32_t Dimension>
Expand All @@ -69,10 +69,11 @@ struct ResolveAccessorAdaptor
}
};

template<typename CascadeAccessor, typename OutputColorType> //NBL_PRIMARY_REQUIRES(ResolveAccessor<CascadeAccessor, typename CascadeAccessor::output_scalar_type, CascadeAccessor::image_dimension>)
template<typename CascadeAccessor, typename OutputColorTypeVec NBL_PRIMARY_REQUIRES(concepts::Vector<OutputColorTypeVec> && ResolveAccessor<CascadeAccessor, typename CascadeAccessor::output_scalar_type, CascadeAccessor::image_dimension>)
struct Resolver
{
using output_type = OutputColorType;
using output_type = OutputColorTypeVec;
using scalar_t = typename vector_traits<output_type>::scalar_type;

struct CascadeSample
{
Expand All @@ -91,13 +92,15 @@ struct Resolver

output_type operator()(NBL_REF_ARG(CascadeAccessor) acc, const int16_t2 coord)
{
float reciprocalBaseI = 1.f;
using scalar_t = typename vector_traits<output_type>::scalar_type;

scalar_t reciprocalBaseI = 1.f;
CascadeSample curr = __sampleCascade(acc, coord, 0u, reciprocalBaseI);

float32_t3 accumulation = float32_t3(0.0f, 0.0f, 0.0f);
float Emin = params.initialEmin;
output_type accumulation = output_type(0.0f, 0.0f, 0.0f);
scalar_t Emin = params.initialEmin;

float prevNormalizedCenterLuma, prevNormalizedNeighbourhoodAverageLuma;
scalar_t prevNormalizedCenterLuma, prevNormalizedNeighbourhoodAverageLuma;
for (int16_t i = 0u; i <= params.lastCascadeIndex; i++)
{
const bool notFirstCascade = i != 0;
Expand All @@ -110,13 +113,13 @@ struct Resolver
next = __sampleCascade(acc, coord, int16_t(i + 1), reciprocalBaseI);
}

float reliability = 1.f;
scalar_t reliability = 1.f;
// sample counting-based reliability estimation
if (params.reciprocalKappa <= 1.f)
{
float localReliability = curr.normalizedCenterLuma;
scalar_t localReliability = curr.normalizedCenterLuma;
// reliability in 3x3 pixel block (see robustness)
float globalReliability = curr.normalizedNeighbourhoodAverageLuma;
scalar_t globalReliability = curr.normalizedNeighbourhoodAverageLuma;
if (notFirstCascade)
{
localReliability += prevNormalizedCenterLuma;
Expand All @@ -130,11 +133,11 @@ struct Resolver
// check if above minimum sampling threshold (avg 9 sample occurences in 3x3 neighbourhood), then use per-pixel reliability (NOTE: tertiary op is in reverse)
reliability = globalReliability < params.reciprocalN ? globalReliability : localReliability;
{
const float accumLuma = acc.calcLuma(accumulation);
const scalar_t accumLuma = acc.calcLuma(accumulation);
if (accumLuma > Emin)
Emin = accumLuma;

const float colorReliability = Emin * reciprocalBaseI * params.colorReliabilityFactor;
const scalar_t colorReliability = Emin * reciprocalBaseI * params.colorReliabilityFactor;

reliability += colorReliability;
reliability *= params.NOverKappa;
Expand All @@ -156,19 +159,18 @@ struct Resolver

// pseudo private stuff:

CascadeSample __sampleCascade(NBL_REF_ARG(CascadeAccessor) acc, int16_t2 coord, uint16_t cascadeIndex, float reciprocalBaseI)
CascadeSample __sampleCascade(NBL_REF_ARG(CascadeAccessor) acc, int16_t2 coord, uint16_t cascadeIndex, scalar_t reciprocalBaseI)
{
typename CascadeAccessor::output_type tmp;
output_type neighbourhood[9];
neighbourhood[0] = acc.template get<float, 2>(coord + int16_t2(-1, -1), cascadeIndex);
neighbourhood[1] = acc.template get<float, 2>(coord + int16_t2(0, -1), cascadeIndex);
neighbourhood[2] = acc.template get<float, 2>(coord + int16_t2(1, -1), cascadeIndex);
neighbourhood[3] = acc.template get<float, 2>(coord + int16_t2(-1, 0), cascadeIndex);
neighbourhood[4] = acc.template get<float, 2>(coord + int16_t2(0, 0), cascadeIndex);
neighbourhood[5] = acc.template get<float, 2>(coord + int16_t2(1, 0), cascadeIndex);
neighbourhood[6] = acc.template get<float, 2>(coord + int16_t2(-1, 1), cascadeIndex);
neighbourhood[7] = acc.template get<float, 2>(coord + int16_t2(0, 1), cascadeIndex);
neighbourhood[8] = acc.template get<float, 2>(coord + int16_t2(1, 1), cascadeIndex);
neighbourhood[0] = acc.template get<scalar_t, 2>(coord + int16_t2(-1, -1), cascadeIndex).xyz;
neighbourhood[1] = acc.template get<scalar_t, 2>(coord + int16_t2(0, -1), cascadeIndex).xyz;
neighbourhood[2] = acc.template get<scalar_t, 2>(coord + int16_t2(1, -1), cascadeIndex).xyz;
neighbourhood[3] = acc.template get<scalar_t, 2>(coord + int16_t2(-1, 0), cascadeIndex).xyz;
neighbourhood[4] = acc.template get<scalar_t, 2>(coord + int16_t2(0, 0), cascadeIndex).xyz;
neighbourhood[5] = acc.template get<scalar_t, 2>(coord + int16_t2(1, 0), cascadeIndex).xyz;
neighbourhood[6] = acc.template get<scalar_t, 2>(coord + int16_t2(-1, 1), cascadeIndex).xyz;
neighbourhood[7] = acc.template get<scalar_t, 2>(coord + int16_t2(0, 1), cascadeIndex).xyz;
neighbourhood[8] = acc.template get<scalar_t, 2>(coord + int16_t2(1, 1), cascadeIndex).xyz;

// numerical robustness
float32_t3 excl_hood_sum = ((neighbourhood[0] + neighbourhood[1]) + (neighbourhood[2] + neighbourhood[3])) +
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