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examples/recipes/dataload/spc_from_pointcloud.py

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+# ==============================================================================================================
+# The following snippet demonstrates how to build kaolin's compressed octree,
+# "Structured Point Cloud (SPC)", from raw point cloud data.
+# ==============================================================================================================
+# See also:
+#
+#  - Tutorial: Understanding Structured Point Clouds (SPCs)
+#    https://github.com/NVIDIAGameWorks/kaolin/blob/master/examples/tutorial/understanding_spcs_tutorial.ipynb
+#
+#  - Documentation: Structured Point Clouds
+#    https://kaolin.readthedocs.io/en/latest/modules/kaolin.ops.spc.html?highlight=spc#kaolin-ops-spc
+# ==============================================================================================================
+
+import torch
+import kaolin
+
+# Create some point data with features
+# Point coordinates are expected to be normalized to the range [-1, 1].
+points = torch.tensor([
+    [-1.0, -1.0, -1.0],
+    [-0.9, -0.95, -1.0],
+    [1.0, 0.0, 0.0],
+    [0.0, -0.1, 0.3],
+    [1.0, 1.0, 1.0]
+], device='cuda')
+features = torch.tensor([
+    [0.1, 1.1, 2.1],
+    [0.2, 1.2, 2.2],
+    [0.3, 1.3, 2.3],
+    [0.4, 1.4, 2.4],
+    [0.5, 1.5, 2.5],
+], device='cuda')
+
+# Structured Point Cloud will be using 3 levels of detail
+level = 3
+
+# In kaolin, operations are batched by default
+# Here, in contrast, we use a single point cloud and therefore invoke an unbatched conversion function.
+# For more information about batched operations, see:
+# https://kaolin.readthedocs.io/en/latest/modules/kaolin.ops.batch.html#kaolin-ops-batch
+spc = kaolin.ops.conversions.pointcloud.unbatched_pointcloud_to_spc(pointcloud=points,
+                                                                    level=level,
+                                                                    features=features)
+
+# SPC is an object which keep tracks of the various octree component
+print(spc)
+print(f'SPC keeps track of the following cells in {level} levels of detail (parents + leaves):\n'
+      f' {spc.point_hierarchies}\n')
+
+# Note that the point cloud coordinates are quantized to integer coordinates.
+# During conversion, when points fall within the same cell, their features are averaged
+print(f'Features for leaf cells:\n {spc.features}')