Improved analytics

casdf/casdf.cu

@@ -243,6 +243,7 @@ void sample_kernel(sample_result result, cumesh mesh, float time)
 		result.points[i] = bary.x * v0 + bary.y * v1 + bary.z * v2;
 		result.barys[i] = bary;
 		result.triangles[i] = tri;
+		result.indices[i] = tindex;
 	}
 }
 
@@ -295,6 +296,7 @@ sample_result sample_result_alloc(uint32_t point_count)
 	cudaMalloc(&result.points, sizeof(glm::vec3) * point_count);
 	cudaMalloc(&result.barys, sizeof(glm::vec3) * point_count);
 	cudaMalloc(&result.triangles, sizeof(glm::uvec3) * point_count);
+	cudaMalloc(&result.indices, sizeof(uint32_t) * point_count);
 
 	return result;
 }

casdf/casdf.hpp

@@ -24,6 +24,8 @@ struct sample_result {
 	glm::vec3 *points;
 	glm::vec3 *barys;
 	glm::uvec3 *triangles;
+	uint32_t *indices;
+	uint32_t *dup;
 	uint32_t point_count;
 };
 

decimate.cpp

@@ -69,12 +69,13 @@ Mesh translate_mesh(const Eigen::MatrixXd &V, const Eigen::MatrixXi &F)
 int main(int argc, char *argv[])
 {
 	// Load arguments
-	if (argc != 2) {
-		printf("Usage: %s <filename>\n", argv[0]);
+	if (argc != 3) {
+		printf("Usage: %s <filename> <proportion>\n", argv[0]);
 		return 1;
 	}
 
 	std::filesystem::path path = std::filesystem::weakly_canonical(argv[1]);
+	float proportion = std::stof(argv[2]);
 
 	// Load mesh
 	Mesh mesh = deduplicate(load_mesh(path)).first;
@@ -87,7 +88,7 @@ int main(int argc, char *argv[])
 	Eigen::VectorXi a0;
 	Eigen::VectorXi a1;
 
-	size_t count = 0.01 * mesh.triangles.size();
+	size_t count = proportion * mesh.triangles.size();
 	if (!igl::decimate(Vertices, Faces, count, V, F, a0, a1)) {
 		printf("Decimation failed\n");
 		return 1;

models.py

@@ -3,7 +3,7 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
-POINT_ENCODING_SIZE = 10
+POINT_ENCODING_SIZE = 25
 
 # SRNM subdivision complex indices
 # TODO: refactor the project to SRCM?
@@ -46,11 +46,11 @@ def __init__(self) -> None:
         c0 = torch.pow(torch.tensor(1), 1.0/len(self.encoding_linears))
         c0 = torch.log(torch.exp(c0) - LipschitzNormalization.one)
         self.regularizers = [
+            LipschitzNormalization(c0),
             LipschitzNormalization(c0),
             # LipschitzNormalization(c0),
             # LipschitzNormalization(c0),
             LipschitzNormalization(c0),
-            LipschitzNormalization(c0),
         ]
 
         self.encoding_linears = nn.ModuleList(self.encoding_linears)

nsc.py

@@ -13,7 +13,7 @@
 from models import *
 
 if len(sys.argv) < 3:
-    print('Usage: python3 srnm.py <sdv_complexes_file> <output_dir>')
+    print('Usage: python3 nsc.py <sdv_complexes_file> <output_dir>')
     sys.exit(1)
 
 sdv_complexes_file = sys.argv[1]
@@ -110,6 +110,14 @@
 
 ps.init()
 
+# for i, tg in enumerate(targets):
+#     tg = tg.reshape(-1, 3)
+#     tg = tg.cpu().detach().numpy()
+#     ps.register_surface_mesh("target-{}".format(i), tg, indices(resolution))
+
+# ps.show()
+# ps.remove_all_structures()
+
 for i, vs in enumerate(vertices):
     vs = vs.reshape(-1, 3)
     vs = vs.cpu().detach().numpy()