(eye, at, up) extraction function

Summary:
Plotly viewing from a specific camera location requires converting that location in to an (eye, at, up) specification. There may be other reasons to want to do this as well. I create a separate utility function for it.

I envisage more such utility functions for manipulating camera information, so I create a separate camera_utils.py file for such things.

Reviewed By: nikhilaravi

Differential Revision: D25981184

fbshipit-source-id: 0947bf98b212676c021f2fddf775bf436dee3487
This commit is contained in:
Jeremy Reizenstein 2021-01-22 07:31:50 -08:00 committed by Facebook GitHub Bot
parent ddebdfbcd7
commit cf9bb7c48c
2 changed files with 115 additions and 0 deletions

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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
from typing import Tuple
import torch
from pytorch3d.transforms import Transform3d
def camera_to_eye_at_up(
world_to_view_transform: Transform3d,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Given a world to view transform, return the eye, at and up vectors which
represent its position.
For example, if cam is a camera object, then after running
.. code-block::
from cameras import look_at_view_transform
eye, at, up = camera_to_eye_at_up(cam.get_world_to_view_transform())
R, T = look_at_view_transform(eye=eye, at=at, up=up)
any other camera created from R and T will have the same world to view
transform as cam.
Also, given a camera position R and T, then after running:
.. code-block::
from cameras import get_world_to_view_transform, look_at_view_transform
eye, at, up = camera_to_eye_at_up(get_world_to_view_transform(R=R, T=T))
R2, T2 = look_at_view_transform(eye=eye, at=at, up=up)
R2 will equal R and T2 will equal T.
Args:
world_to_view_transform: Transform3d representing the extrinsic
transformation of N cameras.
Returns:
eye: FloatTensor of shape [N, 3] representing the camera centers in world space.
at: FloatTensor of shape [N, 3] representing points in world space directly in
front of the cameras e.g. the positions of objects to be viewed by the
cameras.
up: FloatTensor of shape [N, 3] representing vectors in world space which
when projected on to the camera plane point upwards.
"""
cam_trans = world_to_view_transform.inverse()
# In the PyTorch3D right handed coordinate system, the camera in view space
# is always at the origin looking along the +z axis.
# The up vector is not a position so cannot be transformed with
# transform_points. However the position eye+up above the camera
# (whose position vector in the camera coordinate frame is an up vector)
# can be transformed with transform_points.
eye_at_up_view = torch.tensor(
[[0, 0, 0], [0, 0, 1], [0, 1, 0]], dtype=torch.float32, device=cam_trans.device
)
eye_at_up_world = cam_trans.transform_points(eye_at_up_view).reshape(-1, 3, 3)
eye, at, up_plus_eye = eye_at_up_world.unbind(1)
up = up_plus_eye - eye
return eye, at, up

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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import unittest
import torch
from common_testing import TestCaseMixin
from pytorch3d.renderer.camera_utils import camera_to_eye_at_up
from pytorch3d.renderer.cameras import PerspectiveCameras, look_at_view_transform
from torch.nn.functional import normalize
class TestCameraUtils(TestCaseMixin, unittest.TestCase):
def setUp(self) -> None:
torch.manual_seed(42)
def test_invert_eye_at_up(self):
# Generate random cameras and check we can reconstruct their eye, at,
# and up vectors.
N = 13
eye = torch.rand(N, 3)
at = torch.rand(N, 3)
up = torch.rand(N, 3)
R, T = look_at_view_transform(eye=eye, at=at, up=up)
cameras = PerspectiveCameras(R=R, T=T)
eye2, at2, up2 = camera_to_eye_at_up(cameras.get_world_to_view_transform())
# The retrieved eye matches
self.assertClose(eye, eye2, atol=1e-5)
# at-eye as retrieved must be a vector in the same direction as
# the original.
self.assertClose(normalize(at - eye), normalize(at2 - eye2))
# The up vector as retrieved should be rotated the same amount
# around at-eye as the original. The component in the at-eye
# direction is unimportant, as is the length.
# So check that (up x (at-eye)) as retrieved is in the same
# direction as its original value.
up_check = torch.cross(up, at - eye, dim=-1)
up_check2 = torch.cross(up2, at - eye, dim=-1)
self.assertClose(normalize(up_check), normalize(up_check2))
# Master check that we get the same camera if we reinitialise.
R2, T2 = look_at_view_transform(eye=eye2, at=at2, up=up2)
cameras2 = PerspectiveCameras(R=R2, T=T2)
cam_trans = cameras.get_world_to_view_transform()
cam_trans2 = cameras2.get_world_to_view_transform()
self.assertClose(cam_trans.get_matrix(), cam_trans2.get_matrix(), atol=1e-5)