def test_raytrace_with_depth_with_exit(self, octree, point_hierarchy,
pyramid, exsum):
height = 4
width = 4
direction = torch.tensor([[0., 0., -1.]],
dtype=torch.float,
device='cuda').repeat(height * width, 1)
origin = self._generate_rays_origin(height, width, 3)
ridx, pidx, depth = unbatched_raytrace(octree,
point_hierarchy,
pyramid,
exsum,
origin,
direction,
2,
return_depth=True,
with_exit=True)
expected_nuggets = torch.tensor(
[[0, 14], [0, 13], [0, 6], [0, 5], [1, 8], [1, 7], [2, 15],
[4, 10], [4, 9], [5, 12], [5, 11]],
device='cuda',
dtype=torch.int)
assert torch.equal(ridx, expected_nuggets[..., 0])
assert torch.equal(pidx, expected_nuggets[..., 1])
expected_depth = torch.tensor(
[[2.0, 2.5], [2.5, 3.0], [3.0, 3.5], [3.5, 4.0], [3.0, 3.5],
[3.5, 4.0], [3.5, 4.0], [3.0, 3.5], [3.5, 4.0], [3.0, 3.5],
[3.5, 4.0]],
device='cuda',
dtype=torch.float)
assert torch.equal(depth, expected_depth)
def test_mark_first_negative(self, octree, point_hierarchy, pyramid,
exsum):
height = 4
width = 4
direction = torch.tensor([[0., 0., -1.]],
dtype=torch.float,
device='cuda').repeat(height * width, 1)
origin = self._generate_rays_origin(height, width, 3)
ridx, pidx = unbatched_raytrace(octree,
point_hierarchy,
pyramid,
exsum,
origin,
direction,
2,
return_depth=False)
first_hits = mark_pack_boundaries(ridx)
expected_first_hits = torch.tensor([1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0],
device='cuda',
dtype=torch.bool)
assert torch.equal(first_hits, expected_first_hits)
def test_raytrace_none(self, octree, point_hierarchy, pyramid, exsum):
height = 4
width = 4
direction = torch.tensor([[0., 0., 1.]],
dtype=torch.float,
device='cuda').repeat(height * width, 1)
origin = self._generate_rays_origin(height, width, 3)
ridx, pidx, depth = unbatched_raytrace(octree,
point_hierarchy,
pyramid,
exsum,
origin,
direction,
2,
return_depth=True,
with_exit=True)
expected_nuggets = torch.zeros((0, 2), device='cuda', dtype=torch.int)
expected_depths = torch.zeros((0, 2), device='cuda', dtype=torch.float)
assert torch.equal(ridx, expected_nuggets[..., 0])
assert torch.equal(pidx, expected_nuggets[..., 1])
assert torch.equal(depth, expected_depths)
def test_raytrace_positive(self, octree, point_hierarchy, pyramid, exsum):
height = 4
width = 4
direction = torch.tensor([[0., 0., 1.]],
dtype=torch.float,
device='cuda').repeat(height * width, 1)
origin = self._generate_rays_origin(height, width, -3)
ridx, pidx = unbatched_raytrace(octree,
point_hierarchy,
pyramid,
exsum,
origin,
direction,
2,
return_depth=False)
expected_nuggets = torch.tensor(
[[0, 5], [0, 6], [0, 13], [0, 14], [1, 7], [1, 8], [2, 15], [4, 9],
[4, 10], [5, 11], [5, 12]],
device='cuda',
dtype=torch.int)
assert torch.equal(ridx, expected_nuggets[..., 0])
assert torch.equal(pidx, expected_nuggets[..., 1])
def test_raytrace_coarser(self, octree, point_hierarchy, pyramid, exsum):
height = 4
width = 4
direction = torch.tensor([[0., 0., 1.]],
dtype=torch.float,
device='cuda').repeat(height * width, 1)
origin = self._generate_rays_origin(height, width, -3)
ridx, pidx = unbatched_raytrace(octree,
point_hierarchy,
pyramid,
exsum,
origin,
direction,
1,
return_depth=False)
expected_nuggets = torch.tensor(
[[0, 1], [0, 2], [1, 1], [1, 2], [2, 3], [3, 3], [4, 1], [4, 2],
[5, 1], [5, 2], [6, 3], [7, 3], [8, 4], [9, 4], [12, 4], [13, 4]],
device='cuda',
dtype=torch.int)
assert torch.equal(ridx, expected_nuggets[..., 0])
assert torch.equal(pidx, expected_nuggets[..., 1])
def test_ambiguous_raytrace(self):
# TODO(ttakikawa):
# Since 0.10.0, the behaviour of raytracing exactly between voxels
# has been changed from no hits at all to hitting all adjacent voxels.
# This has numerical ramifications because it may cause instability / error
# in the estimation of optical thickness in the volume rendering process
# among other issues. However, we have found that this doesn't lead to any
# obvious visual errors, whereas the no hit case causes speckle noise.
# We will eventually do a more thorough analysis of the numerical consideration of this
# behaviour, but for now we choose to prevent obvious visual errors.
octree = torch.tensor([255], dtype=torch.uint8, device='cuda')
length = torch.tensor([1], dtype=torch.int32)
max_level, pyramids, exsum = scan_octrees(octree, length)
point_hierarchy = generate_points(octree, pyramids, exsum)
origin = torch.tensor([[0., 0., 3.], [3., 3., 3.]],
dtype=torch.float,
device='cuda')
direction = torch.tensor(
[[0., 0., -1.], [-1. / 3., -1. / 3., -1. / 3.]],
dtype=torch.float,
device='cuda')
ridx, pidx, depth = unbatched_raytrace(octree,
point_hierarchy,
pyramids[0],
exsum,
origin,
direction,
1,
return_depth=True)
expected_nuggets = torch.tensor(
[[0, 2], [0, 1], [0, 4], [0, 6], [0, 3], [0, 5], [0, 8], [0, 7],
[1, 8], [1, 1]],
device='cuda',
dtype=torch.int)
assert torch.equal(ridx, expected_nuggets[..., 0])
assert torch.equal(pidx, expected_nuggets[..., 1])
def raytrace(self, ray_o, ray_d, lod):
nugs = spc_render.unbatched_raytrace(self.octree, self.points,
self.pyramid, self.prefix, ray_o,
ray_d, lod + self.base_lod)
return nugs
