Occupancy func with uniform sampling (#564)

Signed-off-by: operel <[email protected]> Occupancy func with uniform sampling - remove warning Signed-off-by: operel <[email protected]> Recipes added to CI Signed-off-by: operel <[email protected]> Co-authored-by: operel <[email protected]>
NVIDIAGameWorks · May 17, 2022 · 0bdaa3a · 0bdaa3a
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diff --git a/ci/gitlab_jenkins_templates/ubuntu_test_CI.jenkins b/ci/gitlab_jenkins_templates/ubuntu_test_CI.jenkins
@@ -61,6 +61,18 @@ spec:
               stage("Understanding SPCs Tutorial") {
                 sh 'cd /kaolin/examples/tutorial && ipython understanding_spcs_tutorial.ipynb'
               }
+            }, "SPC from Pointcloud Recipe": {
+              stage("SPC from Pointcloud Recipe") {
+                sh 'cd /kaolin/examples/recipes/dataload/ && python spc_from_pointcloud.py'
+              }
+            }, "SPC Basics Recipe": {
+              stage("SPC Basics Recipe") {
+                sh 'cd /kaolin/examples/recipes/spc/ && python spc_basics.py'
+              }
+            }, "Occupancy Sampling Recipe": {
+              stage("Occupancy Sampling Recipe") {
+                sh 'cd /kaolin/examples/recipes/preprocess/ && python occupancy_sampling.py'
+              }
             }
           ]
           parallel testMap

diff --git a/examples/recipes/preprocess/occupancy_sampling.py b/examples/recipes/preprocess/occupancy_sampling.py
@@ -0,0 +1,46 @@
+# ==============================================================================================================
+# The following snippet shows how to use kaolin to test sampled values of an occupancy function
+# against a watertight mesh.
+# ==============================================================================================================
+# See also:
+#  - Documentation: Triangular meshes
+#    https://kaolin.readthedocs.io/en/latest/modules/kaolin.ops.mesh.html#triangular-meshes
+# ==============================================================================================================
+
+import torch
+import kaolin
+
+mesh_path = "../../samples/sphere.obj"  # Path to some .obj file with textures
+num_samples = 100000                    # Number of sample points
+
+# 1. Load a watertight mesh from obj file
+mesh = kaolin.io.obj.import_mesh(mesh_path)
+print(f'Loaded mesh with {len(mesh.vertices)} vertices and {len(mesh.faces)} faces.')
+
+# 2. Preprocess mesh:
+# Move tensors to CUDA device
+vertices = mesh.vertices.cuda()
+faces = mesh.faces.cuda()
+
+# Kaolin assumes an exact batch format, we make sure to convert from: (V, 3) to (1, V, 3), where 1 is the batch size
+vertices = vertices.unsqueeze(0)
+
+# 3. Sample random points uniformly in space, from the bounding box of the mesh + 10% margin
+min_bound, _ = vertices.min(dim=1)
+max_bound, _ = vertices.max(dim=1)
+margin = (max_bound - min_bound) * 0.1
+max_bound += margin
+min_bound -= margin
+occupancy_coords = (max_bound - min_bound) * torch.rand(1, num_samples, 3, device='cuda') + min_bound
+
+# 4. Calculate occupancy value
+occupancy_value = kaolin.ops.mesh.check_sign(vertices, faces, occupancy_coords)
+
+# Unbatch to obtain a torch.Tensor of (V, 3) and (V, 1)
+occupancy_coords = occupancy_coords.squeeze(0)
+occupancy_value = occupancy_value.squeeze(0)
+
+percent_in_mesh = torch.count_nonzero(occupancy_value) / len(occupancy_value)
+print(f'Sampled a tensor of points uniformly in space '
+      f'with {occupancy_coords.shape[0]} points of {occupancy_coords.shape[1]}D coordinates.')
+print(f'{"{:.3f}".format(percent_in_mesh)}% of the sampled points are inside the mesh volume.')