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Camera alignment
Summary: adds `corresponding_cameras_alignment` function that estimates a similarity transformation between two sets of cameras. The function is essential for computing camera errors in SfM pipelines. ``` Benchmark Avg Time(μs) Peak Time(μs) Iterations -------------------------------------------------------------------------------- CORRESPONDING_CAMERAS_ALIGNMENT_10_centers_False 32219 36211 16 CORRESPONDING_CAMERAS_ALIGNMENT_10_centers_True 32429 36063 16 CORRESPONDING_CAMERAS_ALIGNMENT_10_extrinsics_False 5548 8782 91 CORRESPONDING_CAMERAS_ALIGNMENT_10_extrinsics_True 6153 9752 82 CORRESPONDING_CAMERAS_ALIGNMENT_100_centers_False 33344 40398 16 CORRESPONDING_CAMERAS_ALIGNMENT_100_centers_True 34528 37095 15 CORRESPONDING_CAMERAS_ALIGNMENT_100_extrinsics_False 5576 7187 90 CORRESPONDING_CAMERAS_ALIGNMENT_100_extrinsics_True 6256 9166 80 CORRESPONDING_CAMERAS_ALIGNMENT_1000_centers_False 32020 37247 16 CORRESPONDING_CAMERAS_ALIGNMENT_1000_centers_True 32776 37644 16 CORRESPONDING_CAMERAS_ALIGNMENT_1000_extrinsics_False 5336 8795 94 CORRESPONDING_CAMERAS_ALIGNMENT_1000_extrinsics_True 6266 9929 80 -------------------------------------------------------------------------------- ``` Reviewed By: shapovalov Differential Revision: D22946415 fbshipit-source-id: 8caae7ee365b304d8aa1f8133cf0dd92c35bc0dd
This commit is contained in:
David Novotny
committed by
Facebook GitHub Bot
Facebook GitHub Bot
parent
14f015d8bf
commit
316b77782e
@@ -26,6 +26,7 @@
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# SOFTWARE.
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import math
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import typing
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import unittest
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import numpy as np
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@@ -47,6 +48,7 @@ from pytorch3d.renderer.cameras import (
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look_at_view_transform,
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)
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from pytorch3d.transforms import Transform3d
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from pytorch3d.transforms.rotation_conversions import random_rotations
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from pytorch3d.transforms.so3 import so3_exponential_map
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@@ -132,6 +134,51 @@ def ndc_to_screen_points_naive(points, imsize):
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return torch.stack((x, y, z), dim=2)
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def init_random_cameras(
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cam_type: typing.Type[CamerasBase], batch_size: int, random_z: bool = False
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):
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cam_params = {}
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T = torch.randn(batch_size, 3) * 0.03
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if not random_z:
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T[:, 2] = 4
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R = so3_exponential_map(torch.randn(batch_size, 3) * 3.0)
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cam_params = {"R": R, "T": T}
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if cam_type in (OpenGLPerspectiveCameras, OpenGLOrthographicCameras):
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cam_params["znear"] = torch.rand(batch_size) * 10 + 0.1
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cam_params["zfar"] = torch.rand(batch_size) * 4 + 1 + cam_params["znear"]
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if cam_type == OpenGLPerspectiveCameras:
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cam_params["fov"] = torch.rand(batch_size) * 60 + 30
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cam_params["aspect_ratio"] = torch.rand(batch_size) * 0.5 + 0.5
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else:
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cam_params["top"] = torch.rand(batch_size) * 0.2 + 0.9
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cam_params["bottom"] = -(torch.rand(batch_size)) * 0.2 - 0.9
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cam_params["left"] = -(torch.rand(batch_size)) * 0.2 - 0.9
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cam_params["right"] = torch.rand(batch_size) * 0.2 + 0.9
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elif cam_type in (FoVPerspectiveCameras, FoVOrthographicCameras):
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cam_params["znear"] = torch.rand(batch_size) * 10 + 0.1
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cam_params["zfar"] = torch.rand(batch_size) * 4 + 1 + cam_params["znear"]
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if cam_type == FoVPerspectiveCameras:
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cam_params["fov"] = torch.rand(batch_size) * 60 + 30
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cam_params["aspect_ratio"] = torch.rand(batch_size) * 0.5 + 0.5
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else:
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cam_params["max_y"] = torch.rand(batch_size) * 0.2 + 0.9
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cam_params["min_y"] = -(torch.rand(batch_size)) * 0.2 - 0.9
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cam_params["min_x"] = -(torch.rand(batch_size)) * 0.2 - 0.9
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cam_params["max_x"] = torch.rand(batch_size) * 0.2 + 0.9
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elif cam_type in (
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SfMOrthographicCameras,
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SfMPerspectiveCameras,
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OrthographicCameras,
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PerspectiveCameras,
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):
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cam_params["focal_length"] = torch.rand(batch_size) * 10 + 0.1
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cam_params["principal_point"] = torch.randn((batch_size, 2))
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else:
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raise ValueError(str(cam_type))
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return cam_type(**cam_params)
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class TestCameraHelpers(TestCaseMixin, unittest.TestCase):
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def setUp(self) -> None:
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super().setUp()
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@@ -410,7 +457,7 @@ class TestCamerasCommon(TestCaseMixin, unittest.TestCase):
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def test_get_camera_center(self, batch_size=10):
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T = torch.randn(batch_size, 3)
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R = so3_exponential_map(torch.randn(batch_size, 3) * 3.0)
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R = random_rotations(batch_size)
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for cam_type in (
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OpenGLPerspectiveCameras,
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OpenGLOrthographicCameras,
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@@ -426,48 +473,6 @@ class TestCamerasCommon(TestCaseMixin, unittest.TestCase):
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C_ = -torch.bmm(R, T[:, :, None])[:, :, 0]
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self.assertTrue(torch.allclose(C, C_, atol=1e-05))
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@staticmethod
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def init_random_cameras(cam_type: CamerasBase, batch_size: int):
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cam_params = {}
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T = torch.randn(batch_size, 3) * 0.03
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T[:, 2] = 4
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R = so3_exponential_map(torch.randn(batch_size, 3) * 3.0)
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cam_params = {"R": R, "T": T}
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if cam_type in (OpenGLPerspectiveCameras, OpenGLOrthographicCameras):
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cam_params["znear"] = torch.rand(batch_size) * 10 + 0.1
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cam_params["zfar"] = torch.rand(batch_size) * 4 + 1 + cam_params["znear"]
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if cam_type == OpenGLPerspectiveCameras:
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cam_params["fov"] = torch.rand(batch_size) * 60 + 30
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cam_params["aspect_ratio"] = torch.rand(batch_size) * 0.5 + 0.5
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else:
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cam_params["top"] = torch.rand(batch_size) * 0.2 + 0.9
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cam_params["bottom"] = -(torch.rand(batch_size)) * 0.2 - 0.9
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cam_params["left"] = -(torch.rand(batch_size)) * 0.2 - 0.9
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cam_params["right"] = torch.rand(batch_size) * 0.2 + 0.9
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elif cam_type in (FoVPerspectiveCameras, FoVOrthographicCameras):
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cam_params["znear"] = torch.rand(batch_size) * 10 + 0.1
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cam_params["zfar"] = torch.rand(batch_size) * 4 + 1 + cam_params["znear"]
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if cam_type == FoVPerspectiveCameras:
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cam_params["fov"] = torch.rand(batch_size) * 60 + 30
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cam_params["aspect_ratio"] = torch.rand(batch_size) * 0.5 + 0.5
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else:
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cam_params["max_y"] = torch.rand(batch_size) * 0.2 + 0.9
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cam_params["min_y"] = -(torch.rand(batch_size)) * 0.2 - 0.9
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cam_params["min_x"] = -(torch.rand(batch_size)) * 0.2 - 0.9
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cam_params["max_x"] = torch.rand(batch_size) * 0.2 + 0.9
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elif cam_type in (
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SfMOrthographicCameras,
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SfMPerspectiveCameras,
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OrthographicCameras,
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PerspectiveCameras,
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):
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cam_params["focal_length"] = torch.rand(batch_size) * 10 + 0.1
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cam_params["principal_point"] = torch.randn((batch_size, 2))
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else:
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raise ValueError(str(cam_type))
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return cam_type(**cam_params)
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@staticmethod
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def init_equiv_cameras_ndc_screen(cam_type: CamerasBase, batch_size: int):
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T = torch.randn(batch_size, 3) * 0.03
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@@ -508,7 +513,7 @@ class TestCamerasCommon(TestCaseMixin, unittest.TestCase):
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PerspectiveCameras,
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):
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# init the cameras
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cameras = TestCamerasCommon.init_random_cameras(cam_type, batch_size)
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cameras = init_random_cameras(cam_type, batch_size)
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# xyz - the ground truth point cloud
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xyz = torch.randn(batch_size, num_points, 3) * 0.3
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# xyz in camera coordinates
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@@ -572,7 +577,7 @@ class TestCamerasCommon(TestCaseMixin, unittest.TestCase):
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):
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# init the cameras
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cameras = TestCamerasCommon.init_random_cameras(cam_type, batch_size)
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cameras = init_random_cameras(cam_type, batch_size)
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# xyz - the ground truth point cloud
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xyz = torch.randn(batch_size, num_points, 3) * 0.3
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# image size
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@@ -618,7 +623,7 @@ class TestCamerasCommon(TestCaseMixin, unittest.TestCase):
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OrthographicCameras,
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PerspectiveCameras,
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):
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cameras = TestCamerasCommon.init_random_cameras(cam_type, batch_size)
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cameras = init_random_cameras(cam_type, batch_size)
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cameras = cameras.to(torch.device("cpu"))
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cameras_clone = cameras.clone()
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