add time interpolation support for ns-render interpolate (#3481)

add time interpolation support

nerfstudio/cameras/camera_paths.py

@@ -39,7 +39,10 @@ def get_interpolated_camera_path(cameras: Cameras, steps: int, order_poses: bool
     """
     Ks = cameras.get_intrinsics_matrices()
     poses = cameras.camera_to_worlds
-    poses, Ks = get_interpolated_poses_many(poses, Ks, steps_per_transition=steps, order_poses=order_poses)
+    times = cameras.times
+    poses, Ks, times = get_interpolated_poses_many(
+        poses, Ks, times, steps_per_transition=steps, order_poses=order_poses
+    )
 
     cameras = Cameras(
         fx=Ks[:, 0, 0],
@@ -48,6 +51,7 @@ def get_interpolated_camera_path(cameras: Cameras, steps: int, order_poses: bool
         cy=Ks[0, 1, 2],
         camera_type=cameras.camera_type[0],
         camera_to_worlds=poses,
+        times=times,
     )
     return cameras
 

nerfstudio/cameras/camera_utils.py

@@ -206,19 +206,40 @@ def get_interpolated_k(
     return Ks
 
 
-def get_ordered_poses_and_k(
+def get_interpolated_time(
+    time_a: Float[Tensor, "1"], time_b: Float[Tensor, "1"], steps: int = 10
+) -> List[Float[Tensor, "1"]]:
+    """
+    Returns interpolated time between two camera poses with specified number of steps.
+
+    Args:
+        time_a: camera time 1
+        time_b: camera time 2
+        steps: number of steps the interpolated pose path should contain
+    """
+    times: List[Float[Tensor, "1"]] = []
+    ts = np.linspace(0, 1, steps)
+    for t in ts:
+        new_t = time_a * (1.0 - t) + time_b * t
+        times.append(new_t)
+    return times
+
+
+def get_ordered_poses_and_k_and_time(
     poses: Float[Tensor, "num_poses 3 4"],
     Ks: Float[Tensor, "num_poses 3 3"],
-) -> Tuple[Float[Tensor, "num_poses 3 4"], Float[Tensor, "num_poses 3 3"]]:
+    times: Optional[Float[Tensor, "num_poses 1"]] = None,
+) -> Tuple[Float[Tensor, "num_poses 3 4"], Float[Tensor, "num_poses 3 3"], Optional[Float[Tensor, "num_poses 1"]]]:
     """
     Returns ordered poses and intrinsics by euclidian distance between poses.
 
     Args:
         poses: list of camera poses
         Ks: list of camera intrinsics
+        times: list of camera times
 
     Returns:
-        tuple of ordered poses and intrinsics
+        tuple of ordered poses, intrinsics and times
 
     """
 
@@ -226,28 +247,33 @@ def get_ordered_poses_and_k(
 
     ordered_poses = torch.unsqueeze(poses[0], 0)
     ordered_ks = torch.unsqueeze(Ks[0], 0)
+    ordered_times = torch.unsqueeze(times[0], 0) if times is not None else None
 
     # remove the first pose from poses
     poses = poses[1:]
     Ks = Ks[1:]
+    times = times[1:] if times is not None else None
 
     for _ in range(poses_num - 1):
         distances = torch.norm(ordered_poses[-1][:, 3] - poses[:, :, 3], dim=1)
         idx = torch.argmin(distances)
         ordered_poses = torch.cat((ordered_poses, torch.unsqueeze(poses[idx], 0)), dim=0)
         ordered_ks = torch.cat((ordered_ks, torch.unsqueeze(Ks[idx], 0)), dim=0)
+        ordered_times = torch.cat((ordered_times, torch.unsqueeze(times[idx], 0)), dim=0) if times is not None else None  # type: ignore
         poses = torch.cat((poses[0:idx], poses[idx + 1 :]), dim=0)
         Ks = torch.cat((Ks[0:idx], Ks[idx + 1 :]), dim=0)
+        times = torch.cat((times[0:idx], times[idx + 1 :]), dim=0) if times is not None else None
 
-    return ordered_poses, ordered_ks
+    return ordered_poses, ordered_ks, ordered_times
 
 
 def get_interpolated_poses_many(
     poses: Float[Tensor, "num_poses 3 4"],
     Ks: Float[Tensor, "num_poses 3 3"],
+    times: Optional[Float[Tensor, "num_poses 1"]] = None,
     steps_per_transition: int = 10,
     order_poses: bool = False,
-) -> Tuple[Float[Tensor, "num_poses 3 4"], Float[Tensor, "num_poses 3 3"]]:
+) -> Tuple[Float[Tensor, "num_poses 3 4"], Float[Tensor, "num_poses 3 3"], Optional[Float[Tensor, "num_poses 1"]]]:
     """Return interpolated poses for many camera poses.
 
     Args:
@@ -261,21 +287,27 @@ def get_interpolated_poses_many(
     """
     traj = []
     k_interp = []
+    time_interp = [] if times is not None else None
 
     if order_poses:
-        poses, Ks = get_ordered_poses_and_k(poses, Ks)
+        poses, Ks, times = get_ordered_poses_and_k_and_time(poses, Ks, times)
 
     for idx in range(poses.shape[0] - 1):
         pose_a = poses[idx].cpu().numpy()
         pose_b = poses[idx + 1].cpu().numpy()
-        poses_ab = get_interpolated_poses(pose_a, pose_b, steps=steps_per_transition)
-        traj += poses_ab
+        traj += get_interpolated_poses(pose_a, pose_b, steps=steps_per_transition)
         k_interp += get_interpolated_k(Ks[idx], Ks[idx + 1], steps=steps_per_transition)
+        if times is not None:
+            time_interp += get_interpolated_time(times[idx], times[idx + 1], steps=steps_per_transition)  # type: ignore
 
     traj = np.stack(traj, axis=0)
     k_interp = torch.stack(k_interp, dim=0)
-
-    return torch.tensor(traj, dtype=torch.float32), torch.tensor(k_interp, dtype=torch.float32)
+    time_interp = torch.stack(time_interp, dim=0) if time_interp is not None else None
+    return (
+        torch.tensor(traj, dtype=torch.float32),
+        torch.tensor(k_interp, dtype=torch.float32),
+        torch.tensor(time_interp, dtype=torch.float32) if time_interp is not None else None,
+    )
 
 
 def normalize(x: torch.Tensor) -> Float[Tensor, "*batch"]: