No more activation inside, guardbands removed

cuda_rasterizer/auxiliary.h

@@ -140,7 +140,7 @@ __forceinline__ __device__ bool in_frustum(int idx,
 	float3 p_proj = { p_hom.x * p_w, p_hom.y * p_w, p_hom.z * p_w };
 	p_view = transformPoint4x3(p_orig, viewmatrix);
 
-	if (p_view.z <= 0.2f || ((p_proj.x < -1.3 || p_proj.x > 1.3 || p_proj.y < -1.3 || p_proj.y > 1.3)))
+	if (p_view.z <= 0.2f)// || ((p_proj.x < -1.3 || p_proj.x > 1.3 || p_proj.y < -1.3 || p_proj.y > 1.3)))
 	{
 		if (prefiltered)
 		{

cuda_rasterizer/backward.cu

@@ -134,8 +134,8 @@ __global__ void computeCov2DCUDA(int P,
 	const float3* means,
 	const int* radii,
 	const float* cov3Ds,
-	float h_x,
-	float h_y,
+	const float h_x, float h_y,
+	const float tan_fovx, float tan_fovy,
 	const float* view_matrix,
 	const float* dL_dconics,
 	float3* dL_dmeans,
@@ -153,11 +153,20 @@ __global__ void computeCov2DCUDA(int P,
 	float3 mean = means[idx];
 	float3 dL_dconic = { dL_dconics[4 * idx], dL_dconics[4 * idx + 1], dL_dconics[4 * idx + 3] };
 	float3 t = transformPoint4x3(mean, view_matrix);
-	float t_inv_norm = 1.f / sqrt(t.x * t.x + t.y * t.y + t.z * t.z);
+
+	const float limx = 1.3f * tan_fovx;
+	const float limy = 1.3f * tan_fovy;
+	const float txtz = t.x / t.z;
+	const float tytz = t.y / t.z;
+	t.x = min(limx, max(-limx, txtz)) * t.z;
+	t.y = min(limy, max(-limy, tytz)) * t.z;
+
+	const float x_grad_mul = txtz < -limx || txtz > limx ? 0 : 1;
+	const float y_grad_mul = tytz < -limy || tytz > limy ? 0 : 1;
 
 	glm::mat3 J = glm::mat3(h_x / t.z, 0.0f, -(h_x * t.x) / (t.z * t.z),
 		0.0f, h_y / t.z, -(h_y * t.y) / (t.z * t.z),
-		t.x * t_inv_norm, t.y * t_inv_norm, t.z * t_inv_norm);
+		0, 0, 0);
 
 	glm::mat3 W = glm::mat3(
 		view_matrix[0], view_matrix[4], view_matrix[8],
@@ -239,8 +248,8 @@ __global__ void computeCov2DCUDA(int P,
 	float tz3 = tz2 * tz;
 
 	// Gradients of loss w.r.t. transformed Gaussian mean t
-	float dL_dtx = -h_x * tz2 * dL_dJ02;
-	float dL_dty = -h_y * tz2 * dL_dJ12;
+	float dL_dtx = x_grad_mul * -h_x * tz2 * dL_dJ02;
+	float dL_dty = y_grad_mul * -h_y * tz2 * dL_dJ12;
 	float dL_dtz = -h_x * tz2 * dL_dJ00 - h_y * tz2 * dL_dJ11 + (2 * h_x * t.x) * tz3 * dL_dJ02 + (2 * h_y * t.y) * tz3 * dL_dJ12;
 
 	// Account for transformation of mean to t
@@ -258,7 +267,7 @@ __global__ void computeCov2DCUDA(int P,
 __device__ void computeCov3D(int idx, const glm::vec3 scale, float mod, const glm::vec4 rot, const float* dL_dcov3Ds, glm::vec3* dL_dscales, glm::vec4* dL_drots)
 {
 	// Recompute (intermediate) results for the 3D covariance computation.
-	glm::vec4 q = rot / glm::length(rot);
+	glm::vec4 q = rot;// / glm::length(rot);
 	float r = q.x;
 	float x = q.y;
 	float y = q.z;
@@ -272,7 +281,7 @@ __device__ void computeCov3D(int idx, const glm::vec3 scale, float mod, const gl
 
 	glm::mat3 S = glm::mat3(1.0f);
 
-	glm::vec3 s = mod * exp(scale);
+	glm::vec3 s = mod * scale;
 	S[0][0] = s.x;
 	S[1][1] = s.y;
 	S[2][2] = s.z;
@@ -298,16 +307,16 @@ __device__ void computeCov3D(int idx, const glm::vec3 scale, float mod, const gl
 	glm::mat3 Rt = glm::transpose(R);
 	glm::mat3 dL_dMt = glm::transpose(dL_dM);
 
-	dL_dMt[0] *= s.x;
-	dL_dMt[1] *= s.y;
-	dL_dMt[2] *= s.z;
-
 	// Gradients of loss w.r.t. scale
 	glm::vec3* dL_dscale = dL_dscales + idx;
 	dL_dscale->x = glm::dot(Rt[0], dL_dMt[0]);
 	dL_dscale->y = glm::dot(Rt[1], dL_dMt[1]);
 	dL_dscale->z = glm::dot(Rt[2], dL_dMt[2]);
 
+	dL_dMt[0] *= s.x;
+	dL_dMt[1] *= s.y;
+	dL_dMt[2] *= s.z;
+
 	// Gradients of loss w.r.t. normalized quaternion
 	glm::vec4 dL_dq;
 	dL_dq.x = 2 * z * (dL_dMt[0][1] - dL_dMt[1][0]) + 2 * y * (dL_dMt[2][0] - dL_dMt[0][2]) + 2 * x * (dL_dMt[1][2] - dL_dMt[2][1]);
@@ -317,7 +326,7 @@ __device__ void computeCov3D(int idx, const glm::vec3 scale, float mod, const gl
 
 	// Gradients of loss w.r.t. unnormalized quaternion
 	float4* dL_drot = (float4*)(dL_drots + idx);
-	*dL_drot = dnormvdv(float4{ rot.x, rot.y, rot.z, rot.w }, float4{ dL_dq.x, dL_dq.y, dL_dq.z, dL_dq.w });
+	*dL_drot = float4{ dL_dq.x, dL_dq.y, dL_dq.z, dL_dq.w };//dnormvdv(float4{ rot.x, rot.y, rot.z, rot.w }, float4{ dL_dq.x, dL_dq.y, dL_dq.z, dL_dq.w });
 }
 
 // Backward pass of the preprocessing steps, except
@@ -377,7 +386,8 @@ __global__ void preprocessCUDA(
 
 // Backward version of the rendering procedure.
 template <uint32_t C>
-__global__ void renderCUDA(
+__global__ void __launch_bounds__(BLOCK_X * BLOCK_Y)
+renderCUDA(
 	const uint2* __restrict__ ranges,
 	const uint32_t* __restrict__ point_list,
 	int W, int H,
@@ -548,6 +558,7 @@ void BACKWARD::preprocess(
 	const float* viewmatrix,
 	const float* projmatrix,
 	const float focal_x, float focal_y,
+	const float tan_fovx, float tan_fovy,
 	const glm::vec3* campos,
 	const float3* dL_dmean2D,
 	const float* dL_dconic,
@@ -569,6 +580,8 @@ void BACKWARD::preprocess(
 		cov3Ds,
 		focal_x,
 		focal_y,
+		tan_fovx,
+		tan_fovy,
 		viewmatrix,
 		dL_dconic,
 		(float3*)dL_dmean3D,

cuda_rasterizer/backward.h

@@ -39,6 +39,7 @@ namespace BACKWARD
 		const float* view,
 		const float* proj,
 		const float focal_x, float focal_y,
+		const float tan_fovx, float tan_fovy,
 		const glm::vec3* campos,
 		const float3* dL_dmean2D,
 		const float* dL_dconics,

cuda_rasterizer/forward.cu

@@ -60,20 +60,25 @@ __device__ glm::vec3 computeColorFromSH(int idx, int deg, int max_coeffs, const
 }
 
 // Forward version of 2D covariance matrix computation
-__device__ float3 computeCov2D(const float3& mean, float focal_x, float focal_y, const float* cov3D, const float* viewmatrix)
+__device__ float3 computeCov2D(const float3& mean, float focal_x, float focal_y, float tan_fovx, float tan_fovy, const float* cov3D, const float* viewmatrix)
 {
 	// The following models the steps outlined by equations 29
 	// and 31 in "EWA Splatting" (Zwicker et al., 2002). 
 	// Additionally considers aspect / scaling of viewport.
 	// Transposes used to account for row-/column-major conventions.
 	float3 t = transformPoint4x3(mean, viewmatrix);
 
-	float t_inv_norm = 1.f / sqrt(t.x * t.x + t.y * t.y + t.z * t.z);
+	const float limx = 1.3f * tan_fovx;
+	const float limy = 1.3f * tan_fovy;
+	const float txtz = t.x / t.z;
+	const float tytz = t.y / t.z;
+	t.x = min(limx, max(-limx, txtz)) * t.z;
+	t.y = min(limy, max(-limy, tytz)) * t.z;
 
 	glm::mat3 J = glm::mat3(
 		focal_x / t.z, 0.0f, -(focal_x * t.x) / (t.z * t.z),
 		0.0f, focal_y / t.z, -(focal_y * t.y) / (t.z * t.z),
-		t.x * t_inv_norm, t.y * t_inv_norm, t.z * t_inv_norm);
+		0, 0, 0);
 
 	glm::mat3 W = glm::mat3(
 		viewmatrix[0], viewmatrix[4], viewmatrix[8],
@@ -98,17 +103,17 @@ __device__ float3 computeCov2D(const float3& mean, float focal_x, float focal_y,
 
 // Forward method for converting scale and rotation properties of each
 // Gaussian to a 3D covariance matrix in world space. Also takes care
-// of quaternion normalization and scale activation via exp.
+// of quaternion normalization.
 __device__ void computeCov3D(const glm::vec3 scale, float mod, const glm::vec4 rot, float* cov3D)
 {
 	// Create scaling matrix
 	glm::mat3 S = glm::mat3(1.0f);
-	S[0][0] = mod * exp(scale.x);
-	S[1][1] = mod * exp(scale.y);
-	S[2][2] = mod * exp(scale.z);
+	S[0][0] = mod * scale.x;
+	S[1][1] = mod * scale.y;
+	S[2][2] = mod * scale.z;
 
 	// Normalize quaternion to get valid rotation
-	glm::vec4 q = rot / glm::length(rot);
+	glm::vec4 q = rot;// / glm::length(rot);
 	float r = q.x;
 	float x = q.y;
 	float y = q.z;
@@ -172,7 +177,7 @@ __global__ void preprocessCUDA(int P, int D, int M,
 	radii[idx] = 0;
 	tiles_touched[idx] = 0;
 
-	// Perform near and frustum culling with guardband, quit if outside.
+	// Perform near culling, quit if outside.
 	float3 p_view;
 	if (!in_frustum(idx, orig_points, viewmatrix, projmatrix, prefiltered, p_view))
 		return;
@@ -196,11 +201,8 @@ __global__ void preprocessCUDA(int P, int D, int M,
 		cov3D = cov3Ds + idx * 6;
 	}
 
-	// Compute max extent of Gaussian for fine-grained fustum culling
-	float max_dist2 = 9.f * max(cov3D[0], max(cov3D[3], cov3D[5]));
-
 	// Compute 2D screen-space covariance matrix
-	float3 cov = computeCov2D(p_orig, focal_x, focal_y, cov3D, viewmatrix);
+	float3 cov = computeCov2D(p_orig, focal_x, focal_y, tan_fovx, tan_fovy, cov3D, viewmatrix);
 
 	// Invert covariance (EWA algorithm)
 	float det = (cov.x * cov.z - cov.y * cov.y);
@@ -209,14 +211,6 @@ __global__ void preprocessCUDA(int P, int D, int M,
 	float det_inv = 1.f / det;
 	float3 conic = { cov.z * det_inv, -cov.y * det_inv, cov.x * det_inv };
 
-	// Fine-grained frustum culling against ellipsoid
-	float z_at_point = p_view.z + sqrt(max_dist2);
-	float x_to_border = z_at_point * tan_fovx;
-	float y_to_border = z_at_point * tan_fovy;
-	float D2_point = p_view.x * p_view.x + p_view.y * p_view.y;
-	if (D2_point - (x_to_border * x_to_border + y_to_border * y_to_border) > max_dist2)
-		return;
-
 	// Compute extent in screen space (by finding eigenvalues of
 	// 2D covariance matrix). Use extent to compute a bounding rectangle
 	// of screen-space tiles that this Gaussian overlaps with. Quit if
@@ -254,7 +248,8 @@ __global__ void preprocessCUDA(int P, int D, int M,
 // block, each thread treats one pixel. Alternates between fetching 
 // and rasterizing data.
 template <uint32_t CHANNELS>
-__global__ void renderCUDA(
+__global__ void __launch_bounds__(BLOCK_X * BLOCK_Y)
+renderCUDA(
 	const uint2* __restrict__ ranges,
 	const uint32_t* __restrict__ point_list,
 	int W, int H,
@@ -407,8 +402,8 @@ void FORWARD::preprocess(int P, int D, int M,
 	const float* projmatrix,
 	const glm::vec3* cam_pos,
 	const int W, int H,
-	const float tan_fovx, float tan_fovy,
 	const float focal_x, float focal_y,
+	const float tan_fovx, float tan_fovy,
 	int* radii,
 	float2* means2D,
 	float* depths,

cuda_rasterizer/forward.h

@@ -24,8 +24,8 @@ namespace FORWARD
 		const float* projmatrix,
 		const glm::vec3* cam_pos,
 		const int W, int H,
-		const float tan_fovx, float tan_fovy,
 		const float focal_x, float focal_y,
+		const float tan_fovx, float tan_fovy,
 		int* radii,
 		float2* points_xy_image,
 		float* depths,

cuda_rasterizer/rasterizer_impl.cu

@@ -247,8 +247,8 @@ int CudaRasterizer::Rasterizer::forward(
 		viewmatrix, projmatrix,
 		(glm::vec3*)cam_pos,
 		width, height,
-		tan_fovx, tan_fovy,
 		focal_x, focal_y,
+		tan_fovx, tan_fovy,
 		radii,
 		geomState.means2D,
 		geomState.depths,
@@ -408,6 +408,7 @@ void CudaRasterizer::Rasterizer::backward(
 		viewmatrix,
 		projmatrix,
 		focal_x, focal_y,
+		tan_fovx, tan_fovy,
 		(glm::vec3*)campos,
 		(float3*)dL_dmean2D,
 		dL_dconic,

rasterize_points.cu

@@ -47,14 +47,13 @@ RasterizeGaussiansCUDA(
   }
 
   const int P = means3D.size(0);
-  const int N = 1; 					// batch size hard-coded
   const int H = image_height;
   const int W = image_width;
 
   auto int_opts = means3D.options().dtype(torch::kInt32);
   auto float_opts = means3D.options().dtype(torch::kFloat32);
 
-  torch::Tensor out_color = torch::full({N, NUM_CHANNELS, H, W}, 0.0, float_opts);
+  torch::Tensor out_color = torch::full({NUM_CHANNELS, H, W}, 0.0, float_opts);
   torch::Tensor radii = torch::full({P}, 0, means3D.options().dtype(torch::kInt32));
 
   torch::Device device(torch::kCUDA);
@@ -126,8 +125,8 @@ std::tuple<torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor, torch::Te
 	const torch::Tensor& imageBuffer) 
 {
   const int P = means3D.size(0);
-  const int H = dL_dout_color.size(2);
-  const int W = dL_dout_color.size(3);
+  const int H = dL_dout_color.size(1);
+  const int W = dL_dout_color.size(2);
 
   int M = 0;
   if(sh.size(0) != 0)