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join_pointclouds_as_scene

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Summary: New function

Reviewed By: davidsonic

Differential Revision: D42776590

fbshipit-source-id: 2a6e73480bcf2d1749f86bcb22d1942e3e8d3167
This commit is contained in:
Jeremy Reizenstein
2023-03-09 06:51:13 -08:00
committed by Facebook GitHub Bot
parent d388881f2c
commit a123815f40
3 changed files with 61 additions and 5 deletions
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22
tests/test_pointclouds.py
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@@ -11,7 +11,11 @@ import unittest
import numpy as np
import torch
from pytorch3d.structures import utils as struct_utils
from pytorch3d.structures.pointclouds import join_pointclouds_as_batch, Pointclouds
from pytorch3d.structures.pointclouds import (
join_pointclouds_as_batch,
join_pointclouds_as_scene,
Pointclouds,
)
from .common_testing import TestCaseMixin
@@ -1159,9 +1163,9 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
normals = [torch.rand(length, 3) for length in lengths]
# Test with normals and features present
pcl = Pointclouds(points=points, features=features, normals=normals)
pcl3 = join_pointclouds_as_batch([pcl] * 3)
check_triple(pcl, pcl3)
pcl1 = Pointclouds(points=points, features=features, normals=normals)
pcl3 = join_pointclouds_as_batch([pcl1] * 3)
check_triple(pcl1, pcl3)
# Test with normals and features present for tensor backed pointclouds
N, P, D = 5, 30, 4
@@ -1173,15 +1177,25 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
pcl3 = join_pointclouds_as_batch([pcl] * 3)
check_triple(pcl, pcl3)
# Test with inconsistent #features
with self.assertRaisesRegex(ValueError, "same number of features"):
join_pointclouds_as_batch([pcl1, pcl])
# Test without normals
pcl_nonormals = Pointclouds(points=points, features=features)
pcl3 = join_pointclouds_as_batch([pcl_nonormals] * 3)
check_triple(pcl_nonormals, pcl3)
pcl_scene = join_pointclouds_as_scene([pcl_nonormals] * 3)
self.assertEqual(len(pcl_scene), 1)
self.assertClose(pcl_scene.features_packed(), pcl3.features_packed())
# Test without features
pcl_nofeats = Pointclouds(points=points, normals=normals)
pcl3 = join_pointclouds_as_batch([pcl_nofeats] * 3)
check_triple(pcl_nofeats, pcl3)
pcl_scene = join_pointclouds_as_scene([pcl_nofeats] * 3)
self.assertEqual(len(pcl_scene), 1)
self.assertClose(pcl_scene.normals_packed(), pcl3.normals_packed())
# Check error raised if all pointclouds in the batch
# are not consistent in including normals/features