create extrinsic from eye point

pytorch3d/renderer/blending.py

@@ -3,7 +3,7 @@
 
 
 import numpy as np
-from typing import NamedTuple
+from typing import NamedTuple, Sequence
 import torch
 
 # Example functions for blending the top K colors per pixel using the outputs
@@ -15,7 +15,7 @@
 class BlendParams(NamedTuple):
     sigma: float = 1e-4
     gamma: float = 1e-4
-    background_color = (1.0, 1.0, 1.0)
+    background_color: Sequence = (1.0, 1.0, 1.0)
 
 
 def hard_rgb_blend(colors, fragments) -> torch.Tensor:

pytorch3d/renderer/cameras.py

@@ -3,7 +3,7 @@
 
 import math
 import numpy as np
-from typing import Tuple
+from typing import Tuple, Sequence
 import torch
 import torch.nn.functional as F
 
@@ -1003,10 +1003,11 @@ def look_at_rotation(
 
 
 def look_at_view_transform(
-    dist,
-    elev,
-    azim,
+    dist=1.0,
+    elev=0.0,
+    azim=0.0,
     degrees: bool = True,
+    eye: Sequence = None,
     at=((0, 0, 0),),  # (1, 3)
     up=((0, 1, 0),),  # (1, 3)
     device="cpu",
@@ -1025,10 +1026,12 @@ def look_at_view_transform(
             reference vector at (1, 0, 0) on the reference plane.
         dist, elem and azim can be of shape (1), (N).
         degrees: boolean flag to indicate if the elevation and azimuth
-            angles are specified in degrees or raidans.
+            angles are specified in degrees or radians.
+        eye: the position of the camera(s) in world coordinates. If eye is not
+            None, it will overide the camera position derived from dist, elev, azim.
         up: the direction of the x axis in the world coordinate system.
         at: the position of the object(s) in world coordinates.
-        up and at can be of shape (1, 3) or (N, 3).
+        eye, up and at can be of shape (1, 3) or (N, 3).
 
     Returns:
         2-element tuple containing
@@ -1039,11 +1042,19 @@ def look_at_view_transform(
     References:
     [0] https://www.scratchapixel.com
     """
-    broadcasted_args = convert_to_tensors_and_broadcast(
-        dist, elev, azim, at, up, device=device
-    )
-    dist, elev, azim, at, up = broadcasted_args
-    C = camera_position_from_spherical_angles(dist, elev, azim, device=device)
+
+    if eye is not None:
+        broadcasted_args = convert_to_tensors_and_broadcast(
+            eye, at, up, device=device)
+        eye, at, up = broadcasted_args
+        C = eye
+    else:
+        broadcasted_args = convert_to_tensors_and_broadcast(
+            dist, elev, azim, at, up, device=device)
+        dist, elev, azim, at, up = broadcasted_args
+        C = camera_position_from_spherical_angles(
+            dist, elev, azim, degrees=degrees, device=device)
+
     R = look_at_rotation(C, at, up, device=device)
     T = -torch.bmm(R.transpose(1, 2), C[:, :, None])[:, :, 0]
     return R, T

tests/test_cameras.py

@@ -39,6 +39,7 @@
     camera_position_from_spherical_angles,
     get_world_to_view_transform,
     look_at_rotation,
+    look_at_view_transform,
 )
 from pytorch3d.transforms import Transform3d
 from pytorch3d.transforms.so3 import so3_exponential_map
@@ -117,6 +118,34 @@ def setUp(self) -> None:
         torch.manual_seed(42)
         np.random.seed(42)
 
+    def test_look_at_view_transform_from_eye_point_tuple(self):
+        dist = math.sqrt(2)
+        elev = math.pi / 4
+        azim = 0.0
+        eye = ((0.0, 1.0, 1.0), )
+        # using passed values for dist, elev, azim
+        R, t = look_at_view_transform(dist, elev, azim, degrees=False)
+        # using other values for dist, elev, azim - eye overrides
+        R_eye, t_eye = look_at_view_transform(dist=3, elev=2, azim=1, eye=eye)
+        # using only eye value
+        R_eye_only, t_eye_only = look_at_view_transform(eye=eye)
+        self.assertTrue(torch.allclose(R, R_eye, atol=2e-7))
+        self.assertTrue(torch.allclose(t, t_eye, atol=2e-7))
+        self.assertTrue(torch.allclose(R, R_eye_only, atol=2e-7))
+        self.assertTrue(torch.allclose(t, t_eye_only, atol=2e-7))
+
+    def test_look_at_view_transform_default_values(self):
+        dist = 1.0
+        elev = 0.0
+        azim = 0.0
+        # Using passed values for dist, elev, azim
+        R, t = look_at_view_transform(dist, elev, azim)
+        # Using default dist=1.0, elev=0.0, azim=0.0
+        R_default, t_default = look_at_view_transform()
+        # test default = passed = expected
+        self.assertTrue(torch.allclose(R, R_default, atol=2e-7))
+        self.assertTrue(torch.allclose(t, t_default, atol=2e-7))
+
     def test_camera_position_from_angles_python_scalar(self):
         dist = 2.7
         elev = 90.0