423A35C7/pytorch3d
1
0
Fork 0
mirror of https://github.com/facebookresearch/pytorch3d.git synced 2025-08-02 03:42:50 +08:00
Code Issues Packages Projects Releases Wiki Activity

Update NDC raysampler for non-square convention (#29)

Browse Source 
Summary:
- Old NDC convention had xy coords in [-1,1]x[-1,1]
- New NDC convention has xy coords in [-1, 1]x[-u, u] or [-u, u]x[-1, 1]

where u > 1 is the aspect ratio of the image.

This PR fixes the NDC raysampler to use the new convention.

Partial fix for https://github.com/facebookresearch/pytorch3d/issues/868

Pull Request resolved: https://github.com/fairinternal/pytorch3d/pull/29

Reviewed By: davnov134

Differential Revision: D31926148

Pulled By: bottler

fbshipit-source-id: c6c42c60d1473b04e60ceb49c8c10951ddf03c74
This commit is contained in:
Ignacio Rocco 2021-11-05 10:28:51 -07:00 committed by Facebook GitHub Bot
parent bfeb82efa3
commit cac6cb1b78
4 changed files with 118 additions and 35 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

23
pytorch3d/renderer/implicit/raysampling.py
View File

@ -139,8 +139,8 @@ class NDCGridRaysampler(GridRaysampler):
have uniformly-spaced z-coordinates between a predefined minimum and maximum depth.
`NDCGridRaysampler` follows the screen conventions of the `Meshes` and `Pointclouds`
renderers. I.e. the border of the leftmost / rightmost / topmost / bottommost pixel
has coordinates 1.0 / -1.0 / 1.0 / -1.0 respectively.
renderers. I.e. the pixel coordinates are in [-1, 1]x[-u, u] or [-u, u]x[-1, 1]
where u > 1 is the aspect ratio of the image.
"""
def __init__(
@ -159,13 +159,20 @@ class NDCGridRaysampler(GridRaysampler):
min_depth: The minimum depth of a ray-point.
max_depth: The maximum depth of a ray-point.
"""
half_pix_width = 1.0 / image_width
half_pix_height = 1.0 / image_height
if image_width >= image_height:
range_x = image_width / image_height
range_y = 1.0
else:
range_x = 1.0
range_y = image_height / image_width
half_pix_width = range_x / image_width
half_pix_height = range_y / image_height
super().__init__(
min_x=1.0 - half_pix_width,
max_x=-1.0 + half_pix_width,
min_y=1.0 - half_pix_height,
max_y=-1.0 + half_pix_height,
min_x=range_x - half_pix_width,
max_x=-range_x + half_pix_width,
min_y=range_y - half_pix_height,
max_y=-range_y + half_pix_height,
image_width=image_width,
image_height=image_height,
n_pts_per_ray=n_pts_per_ray,

82
tests/test_raysampling.py
View File

@ -24,6 +24,69 @@ from pytorch3d.transforms import Rotate
from test_cameras import init_random_cameras
class TestNDCRaysamplerConvention(TestCaseMixin, unittest.TestCase):
def setUp(self) -> None:
torch.manual_seed(42)
def test_ndc_convention(
self,
h=428,
w=760,
):
device = torch.device("cuda")
camera = init_random_cameras(PerspectiveCameras, 1, random_z=True).to(device)
depth_map = torch.ones((1, 1, h, w)).to(device)
xyz = ray_bundle_to_ray_points(
NDCGridRaysampler(
image_width=w,
image_height=h,
n_pts_per_ray=1,
min_depth=1.0,
max_depth=1.0,
)(camera)._replace(lengths=depth_map[:, 0, ..., None])
).view(1, -1, 3)
# project pointcloud
xy = camera.transform_points(xyz)[:, :, :2].squeeze()
xy_grid = self._get_ndc_grid(h, w, device)
self.assertClose(
xy,
xy_grid,
atol=1e-4,
)
def _get_ndc_grid(self, h, w, device):
if w >= h:
range_x = w / h
range_y = 1.0
else:
range_x = 1.0
range_y = h / w
half_pix_width = range_x / w
half_pix_height = range_y / h
min_x = range_x - half_pix_width
max_x = -range_x + half_pix_width
min_y = range_y - half_pix_height
max_y = -range_y + half_pix_height
y_grid, x_grid = torch.meshgrid(
torch.linspace(min_y, max_y, h, dtype=torch.float32),
torch.linspace(min_x, max_x, w, dtype=torch.float32),
)
x_points = x_grid.contiguous().view(-1).to(device)
y_points = y_grid.contiguous().view(-1).to(device)
xy = torch.stack((x_points, y_points), dim=1)
return xy
class TestRaysampling(TestCaseMixin, unittest.TestCase):
def setUp(self) -> None:
torch.manual_seed(42)
@ -147,12 +210,19 @@ class TestRaysampling(TestCaseMixin, unittest.TestCase):
if issubclass(raysampler_type, NDCGridRaysampler):
# adjust the gt bounds for NDCGridRaysampler
half_pix_width = 1.0 / image_width
half_pix_height = 1.0 / image_height
min_x_ = 1.0 - half_pix_width
max_x_ = -1.0 + half_pix_width
min_y_ = 1.0 - half_pix_height
max_y_ = -1.0 + half_pix_height
if image_width >= image_height:
range_x = image_width / image_height
range_y = 1.0
else:
range_x = 1.0
range_y = image_height / image_width
half_pix_width = range_x / image_width
half_pix_height = range_y / image_height
min_x_ = range_x - half_pix_width
max_x_ = -range_x + half_pix_width
min_y_ = range_y - half_pix_height
max_y_ = -range_y + half_pix_height
else:
min_x_ = min_x
max_x_ = max_x

24
tests/test_render_implicit.py
View File

@ -159,8 +159,12 @@ class TestRenderImplicit(TestCaseMixin, unittest.TestCase):
with self.assertRaises(ValueError):
renderer(cameras=cameras, volumetric_function=bad_volumetric_function)
def test_compare_with_meshes_renderer(
self, batch_size=11, image_size=100, sphere_diameter=0.6
def test_compare_with_meshes_renderer(self):
self._compare_with_meshes_renderer(image_size=(200, 100))
self._compare_with_meshes_renderer(image_size=(100, 200))
def _compare_with_meshes_renderer(
self, image_size, batch_size=11, sphere_diameter=0.6
):
"""
Generate a spherical RGB volumetric function and its corresponding mesh
@ -169,9 +173,7 @@ class TestRenderImplicit(TestCaseMixin, unittest.TestCase):
"""
# generate NDC camera extrinsics and intrinsics
cameras = init_cameras(
batch_size, image_size=[image_size, image_size], ndc=True
)
cameras = init_cameras(batch_size, image_size=image_size, ndc=True)
# get rand offset of the volume
sphere_centroid = torch.randn(batch_size, 3, device=cameras.device) * 0.1
@ -179,8 +181,8 @@ class TestRenderImplicit(TestCaseMixin, unittest.TestCase):
# init the grid raysampler with the ndc grid
raysampler = NDCGridRaysampler(
image_width=image_size,
image_height=image_size,
image_width=image_size[1],
image_height=image_size[0],
n_pts_per_ray=256,
min_depth=0.1,
max_depth=2.0,
@ -336,9 +338,11 @@ class TestRenderImplicit(TestCaseMixin, unittest.TestCase):
self.assertClose(mu_diff, torch.zeros_like(mu_diff), atol=5e-2)
self.assertClose(std_diff, torch.zeros_like(std_diff), atol=6e-2)
def test_rotating_gif(
self, n_frames=50, fps=15, image_size=(100, 100), sphere_diameter=0.5
):
def test_rotating_gif(self):
self._rotating_gif(image_size=(200, 100))
self._rotating_gif(image_size=(100, 200))
def _rotating_gif(self, image_size, n_frames=50, fps=15, sphere_diameter=0.5):
"""
Render a gif animation of a rotating sphere (runs only if `DEBUG==True`).
"""

24
tests/test_render_volumes.py
View File

@ -164,7 +164,7 @@ def init_cameras(
p0 = torch.ones(batch_size, 2, device=device)
p0[:, 0] *= image_size[1] * 0.5
p0[:, 1] *= image_size[0] * 0.5
focal = image_size[0] * torch.ones(batch_size, device=device)
focal = max(*image_size) * torch.ones(batch_size, device=device)
# convert to a Camera object
cameras = PerspectiveCameras(focal, p0, R=R, T=T, device=device)
@ -295,7 +295,7 @@ class TestRenderVolumes(TestCaseMixin, unittest.TestCase):
_validate_ray_bundle_variables(*bad_ray_bundle)
def test_compare_with_pointclouds_renderer(
self, batch_size=11, volume_size=(30, 30, 30), image_size=200
self, batch_size=11, volume_size=(30, 30, 30), image_size=(200, 250)
):
"""
Generate a volume and its corresponding point cloud and check whether
@ -303,9 +303,7 @@ class TestRenderVolumes(TestCaseMixin, unittest.TestCase):
"""
# generate NDC camera extrinsics and intrinsics
cameras = init_cameras(
batch_size, image_size=[image_size, image_size], ndc=True
)
cameras = init_cameras(batch_size, image_size=image_size, ndc=True)
# init the boundary volume
for shape in ("sphere", "cube"):
@ -340,10 +338,10 @@ class TestRenderVolumes(TestCaseMixin, unittest.TestCase):
# init the grid raysampler with the ndc grid
coord_range = 1.0
half_pix_size = coord_range / image_size
half_pix_size = coord_range / max(*image_size)
raysampler = NDCGridRaysampler(
image_width=image_size,
image_height=image_size,
image_width=image_size[1],
image_height=image_size[0],
n_pts_per_ray=256,
min_depth=0.1,
max_depth=2.0,
@ -499,8 +497,12 @@ class TestRenderVolumes(TestCaseMixin, unittest.TestCase):
images_opacities_mc.permute(0, 3, 1, 2), images_opacities_mc_, atol=1e-4
)
def test_rotating_gif(
self, n_frames=50, fps=15, volume_size=(100, 100, 100), image_size=(100, 100)
def test_rotating_gif(self):
self._rotating_gif(image_size=(200, 100))
self._rotating_gif(image_size=(100, 200))
def _rotating_gif(
self, image_size, n_frames=50, fps=15, volume_size=(100, 100, 100)
):
"""
Render a gif animation of a rotating cube/sphere (runs only if `DEBUG==True`).
@ -586,7 +588,7 @@ class TestRenderVolumes(TestCaseMixin, unittest.TestCase):
# batch_size = 4 sides of the cube
batch_size = 4
image_size = (50, 50)
image_size = (50, 40)
for volume_size in ([25, 25, 25],):
for sample_mode in ("bilinear", "nearest"):