55 lines
3.1 KiB
Python
55 lines
3.1 KiB
Python
# ==============================================================================================================
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# The following snippet demonstrates the basic usage of kaolin's compressed octree,
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# termed "Structured Point Cloud (SPC)".
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# Note this is a low level structure: practitioners are encouraged to visit the references below.
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# ==============================================================================================================
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# See also:
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#
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# - Code: kaolin.ops.spc.SPC
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# https://kaolin.readthedocs.io/en/latest/modules/kaolin.rep.html?highlight=SPC#kaolin.rep.Spc
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#
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# - Tutorial: Understanding Structured Point Clouds (SPCs)
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# https://github.com/NVIDIAGameWorks/kaolin/blob/master/examples/tutorial/understanding_spcs_tutorial.ipynb
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#
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# - Documentation: Structured Point Clouds
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# https://kaolin.readthedocs.io/en/latest/modules/kaolin.ops.spc.html?highlight=spc#kaolin-ops-spc
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# ==============================================================================================================
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import torch
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import kaolin
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# Construct SPC from some points data. Point coordinates are expected to be normalized to the range [-1, 1].
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points = torch.tensor([[-1.0, -1.0, -1.0], [-0.9, -0.95, -1.0], [1.0, 1.0, 1.0]], device='cuda')
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# In kaolin, operations are batched by default
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# Here, in contrast, we use a single point cloud and therefore invoke an unbatched conversion function.
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# The Structured Point Cloud will be using 3 levels of detail
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spc = kaolin.ops.conversions.pointcloud.unbatched_pointcloud_to_spc(pointcloud=points, level=3)
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# SPC is a batched object, and most of its fields are packed.
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# (see: https://kaolin.readthedocs.io/en/latest/modules/kaolin.ops.batch.html#kaolin-ops-batch )
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# spc.length defines the boundaries between different batched SPC instances the same object holds.
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# Here we keep track of a single entry batch, which has 8 octree non-leaf cells.
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print(f'spc.batch_size: {spc.batch_size}')
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print(f'spc.lengths (cells per batch entry): {spc.lengths}')
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# SPC is hierarchical and keeps information for every level of detail from 0 to 3.
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# spc.point_hierarchies keeps the sparse, zero indexed coordinates of each occupied cell, per level.
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print(f'SPC keeps track of total of {spc.point_hierarchies.shape[0]} parent + leaf cells:')
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# To separate the boundaries, the spc.pyramids field is used.
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# This field is not-packed, unlike the other SPC fields.
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pyramid_of_first_entry_in_batch = spc.pyramids[0]
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cells_per_level = pyramid_of_first_entry_in_batch[0]
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cumulative_cells_per_level = pyramid_of_first_entry_in_batch[1]
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for i, lvl_cells in enumerate(cells_per_level[:-1]):
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print(f'LOD #{i} has {lvl_cells} cells.')
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# The spc.octrees field keeps track of the fundamental occupancy information of each cell in the octree.
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print('The occupancy of each octant parent cell, in Morton / Z-curve order is:')
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print(['{0:08b}'.format(octree_byte) for octree_byte in spc.octrees])
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# Since SPCs are low level objects, they require bookkeeping of multiple fields.
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# For ease of use, these fields are collected and tracked within a single class: kaolin.ops.spc.SPC
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# See references at the header for elaborate information on how to use this object.
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