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add from_ndc to unproject_points

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Summary: Give unproject_points an argument letting it bypass screen space. use it to let the raysampler work for cameras defined in screen space.

Reviewed By: gkioxari

Differential Revision: D32596600

fbshipit-source-id: 2fe585dcd138cdbc65dd1c70e1957fd894512d3d
This commit is contained in:
Jeremy Reizenstein 2021-12-07 15:02:46 -08:00 committed by Facebook GitHub Bot
parent a0e2d2e3c3
commit cff4876131
2 changed files with 39 additions and 3 deletions
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40
pytorch3d/renderer/cameras.py
View File

@ -129,6 +129,10 @@ class CamerasBase(TensorProperties):
coordinates using the camera extrinsics `R` and `T`.
`False` ignores `R` and `T` and unprojects to
the camera view coordinates.
from_ndc: If `False` (default), assumes xy part of input is in
NDC space if self.in_ndc(), otherwise in screen space. If
`True`, assumes xy is in NDC space even if the camera
is defined in screen space.
Returns
new_points: unprojected points with the same shape as `xy_depth`.
@ -998,12 +1002,27 @@ class PerspectiveCameras(CamerasBase):
return transform
def unproject_points(
self, xy_depth: torch.Tensor, world_coordinates: bool = True, **kwargs
self,
xy_depth: torch.Tensor,
world_coordinates: bool = True,
from_ndc: bool = False,
**kwargs
) -> torch.Tensor:
"""
Args:
from_ndc: If `False` (default), assumes xy part of input is in
NDC space if self.in_ndc(), otherwise in screen space. If
`True`, assumes xy is in NDC space even if the camera
is defined in screen space.
"""
if world_coordinates:
to_camera_transform = self.get_full_projection_transform(**kwargs)
else:
to_camera_transform = self.get_projection_transform(**kwargs)
if from_ndc:
to_camera_transform = to_camera_transform.compose(
self.get_ndc_camera_transform()
)
unprojection_transform = to_camera_transform.inverse()
xy_inv_depth = torch.cat(
@ -1030,6 +1049,8 @@ class PerspectiveCameras(CamerasBase):
For cameras defined in screen space, we adjust the principal point computation
which is defined in the image space (commonly) and scale the points to NDC space.
This transform leaves the depth unchanged.
Important: This transforms assumes PyTorch3D conventions for the input points,
i.e. +X left, +Y up.
"""
@ -1199,12 +1220,27 @@ class OrthographicCameras(CamerasBase):
return transform
def unproject_points(
self, xy_depth: torch.Tensor, world_coordinates: bool = True, **kwargs
self,
xy_depth: torch.Tensor,
world_coordinates: bool = True,
from_ndc: bool = False,
**kwargs
) -> torch.Tensor:
"""
Args:
from_ndc: If `False` (default), assumes xy part of input is in
NDC space if self.in_ndc(), otherwise in screen space. If
`True`, assumes xy is in NDC space even if the camera
is defined in screen space.
"""
if world_coordinates:
to_camera_transform = self.get_full_projection_transform(**kwargs)
else:
to_camera_transform = self.get_projection_transform(**kwargs)
if from_ndc:
to_camera_transform = to_camera_transform.compose(
self.get_ndc_camera_transform()
)
unprojection_transform = to_camera_transform.inverse()
return unprojection_transform.transform_points(xy_depth)

2
pytorch3d/renderer/implicit/raysampling.py
View File

@ -312,7 +312,7 @@ def _xy_to_ray_bundle(
)
# unproject the points
unprojected = cameras.unproject_points(to_unproject) # pyre-ignore
unprojected = cameras.unproject_points(to_unproject, from_ndc=True) # pyre-ignore
# split the two planes back
rays_plane_1_world = unprojected[:, :n_rays_per_image]