GAMES202——作业5 实时光线追踪降噪（联合双边滤波、多帧的投影与积累、À-Trous Wavelet 加速单帧降噪）

本文主要是介绍GAMES202——作业5 实时光线追踪降噪（联合双边滤波、多帧的投影与积累、À-Trous Wavelet 加速单帧降噪），希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

任务

1.实现单帧降噪

2.实现多帧投影

3.实现多帧累积

Bonus:使用À-Trous Wavelet 加速单帧降噪

实现

单帧降噪

这里实现比较简单，直接根据给出的联合双边滤波核的公式就能实现

Buffer2D<Float3> Denoiser::Filter(const FrameInfo &frameInfo) {int height = frameInfo.m_beauty.m_height;int width = frameInfo.m_beauty.m_width;Buffer2D<Float3> filteredImage = CreateBuffer2D<Float3>(width, height);int kernelRadius = 16;
#pragma omp parallel forfor (int y = 0; y < height; y++) {for (int x = 0; x < width; x++) {// TODO: Joint bilateral filterint x_min = std::max(0,x-kernelRadius);int x_max = std::min(width-1,x+kernelRadius);int y_min = std::max(0,y-kernelRadius);int y_max = std::min(height-1,y+kernelRadius);auto center_color = frameInfo.m_beauty(x,y);auto center_normal = frameInfo.m_normal(x,y);auto center_positon = frameInfo.m_position(x,y);Float3 finalColor;float weight = 0.0f;for(int i = x_min ; i<=x_max ; i++){for(int j = y_min ; j<=y_max ; j++){auto position = frameInfo.m_position(i,j);auto normal = frameInfo.m_normal(i,j);auto color = frameInfo.m_beauty(i,j);//firstfloat distance2 = SqrDistance(position,center_positon);float P = distance2 / ( 2 * m_sigmaCoord * m_sigmaCoord);//secondauto color2 = SqrDistance(color,center_color);float C = color2 / (2 * m_sigmaColor * m_sigmaColor);//thirdauto N = SafeAcos(Dot(center_normal,normal));N *=N;N / (2.0 * m_sigmaNormal * m_sigmaNormal);  //youwenti//  std::cout << N << std::endl;// auto normal2 = SafeAcos(Dot(center_normal,normal));// normal2 *= normal2;// auto N = normal2 / ( 2 * m_sigmaNormal * m_sigmaNormal);//forthfloat D = 0.0;//not self to makesure not/0if(P > 0.0f){auto direction = Normalize( position - center_positon );float plane = Dot(direction , center_normal);float plane2 = plane * plane;D = plane2 / ( 2 * m_sigmaPlane * m_sigmaPlane);}//finalfloat temp = std::exp(-P-C-N-D);finalColor += color * temp;weight += temp;}}filteredImage(x,y) = finalColor / weight;// filteredImage(x, y) = Float3(0.0);}}return filteredImage;
}

帧的投影

根据上面的式子，能够求出世界坐标系在上一帧的屏幕坐标的位置。求出位置后，需要先判断这个位置是否超出了屏幕坐标。如果在屏幕内，判断是否为同一个物体，如果不是就不能采用上一帧的信息，否则会造成拖影现象。

void Denoiser::Reprojection(const FrameInfo &frameInfo) {int height = m_accColor.m_height;int width = m_accColor.m_width;Matrix4x4 preWorldToScreen =m_preFrameInfo.m_matrix[m_preFrameInfo.m_matrix.size() - 1];Matrix4x4 preWorldToCamera =m_preFrameInfo.m_matrix[m_preFrameInfo.m_matrix.size() - 2];
#pragma omp parallel forfor (int y = 0; y < height; y++) {for (int x = 0; x < width; x++) {// TODO: Reprojectauto id = frameInfo.m_id(x,y);auto world_pos = frameInfo.m_position(x,y);m_valid(x, y) = false;m_misc(x, y) = Float3(0.f);// std::cout << id  << std::endl;if(id == -1)continue;auto world_to_local = Inverse(frameInfo.m_matrix[id]);auto local_to_pre_world = m_preFrameInfo.m_matrix[id];auto local_pos = world_to_local(world_pos,Float3::EType::Point);auto pre_world_pos = local_to_pre_world(local_pos,Float3::EType::Point);auto pre_screen_coord = preWorldToScreen(pre_world_pos,Float3::EType::Point);if(pre_screen_coord.x<0 || pre_screen_coord.x>=width || pre_screen_coord.y<0 || pre_screen_coord.y >=height){continue;}else{auto pre_id = m_preFrameInfo.m_id(int(pre_screen_coord.x),int(pre_screen_coord.y));if(pre_id == id){m_valid(x,y) = true;m_misc(x,y) = m_accColor(int(pre_screen_coord.x),int(pre_screen_coord.y));}}}}std::swap(m_misc, m_accColor);
}

帧的累积

先判断某个像素是否存在于上一帧里，如果存在，那么就按照α来进行插值沿用上一帧。如果不存在，说明该像素不能以上一帧进行参考，将α设置为1，只用自己这一帧。

对于Clamp部分，首先需要计算 Ci 在 7×7 的邻域内的均值 µ 和方差 σ，然后我们将上一帧的颜色限制在 (µ − kσ, µ + kσ) 范围内。

void Denoiser::TemporalAccumulation(const Buffer2D<Float3> &curFilteredColor) {int height = m_accColor.m_height;int width = m_accColor.m_width;int kernelRadius = 3;
#pragma omp parallel forfor (int y = 0; y < height; y++) {for (int x = 0; x < width; x++) {// TODO: Temporal clampFloat3 color = m_accColor(x, y);float alpha = 1.0f; if(m_valid(x,y)){alpha = m_alpha;int x_min = std::max(0,x-kernelRadius);int x_max = std::min(width-1,x+kernelRadius);int y_min = std::max(0,y-kernelRadius);int y_max = std::min(height-1,y+kernelRadius);auto mu = Float3(0.0);auto sigma = Float3(0.0);for(int i =x_min;i<=x_max;i++){for(int j=y_min;j<=y_max;j++){mu += curFilteredColor(i,j);sigma += Sqr(curFilteredColor(i,j)-curFilteredColor(x,y));}}int count = kernelRadius * 2 + 1;count *= count;mu = mu / float(count);sigma = SafeSqrt( sigma / float(count));// mu = mu / ( (x_max-x_min) * (y_max - y_min) );// sigma = sigma / ( (x_max-x_min) * (y_max - y_min) );color = Clamp(color,mu - sigma * m_colorBoxK,mu + sigma * m_colorBoxK );}// TODO: Exponential moving averagem_misc(x, y) = Lerp(color, curFilteredColor(x, y), alpha);}}std::swap(m_misc, m_accColor);
}

À-Trous Wavelet 加速单帧降噪

课程里给出了一维的解释。由于没有学过信号与系统，这里我的简单理解是离得越远，点的贡献就越小，那么在远的地方就选一个点来代表其附近区域的贡献。

Buffer2D<Float3> Denoiser::AFilter(const FrameInfo &frameInfo) {int height = frameInfo.m_beauty.m_height;int width = frameInfo.m_beauty.m_width;Buffer2D<Float3> filteredImage = CreateBuffer2D<Float3>(width, height);int kernelRadius = 16;
#pragma omp parallel forfor (int y = 0; y < height; y++) {for (int x = 0; x < width; x++) {// TODO: Joint bilateral filterint x_min = std::max(0,x-kernelRadius);int x_max = std::min(width-1,x+kernelRadius);int y_min = std::max(0,y-kernelRadius);int y_max = std::min(height-1,y+kernelRadius);auto center_color = frameInfo.m_beauty(x,y);auto center_normal = frameInfo.m_normal(x,y);auto center_positon = frameInfo.m_position(x,y);Float3 finalColor;float weight = 0.0f;int passes = 6;for(int pass = 0;pass < passes;pass++){for(int filterX = -3 ; filterX <=3; filterX++){for(int filterY = -3 ; filterY <= 3; filterY++){int m = x + std::pow(2,pass)*filterX;int n = y + std::pow(2,pass)*filterY;auto position = frameInfo.m_position(m,n);auto normal = frameInfo.m_normal(m,n);auto color = frameInfo.m_beauty(m,n);//firstfloat distance2 = SqrDistance(position,center_positon);float P = distance2 / ( 2 * m_sigmaCoord * m_sigmaCoord);//secondauto color2 = SqrDistance(color,center_color);float C = color2 / (2 * m_sigmaColor * m_sigmaColor);//thirdauto N = SafeAcos(Dot(center_normal,normal));N *=N;N / (2.0 * m_sigmaNormal * m_sigmaNormal);  //youwenti//forthfloat D = 0.0;//not self to makesure not/0if(P > 0.0f){auto direction = Normalize( position - center_positon );float plane = Dot(direction , center_normal);float plane2 = plane * plane;D = plane2 / ( 2 * m_sigmaPlane * m_sigmaPlane);}//finalfloat temp = std::exp(-P-C-N-D);finalColor += color * temp;weight += temp;}}}filteredImage(x,y) = finalColor / weight;// filteredImage(x, y) = Float3(0.0);}}return filteredImage;
}