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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from pytorch3d.transforms.rotation_conversions import (
matrix_to_quaternion,
quaternion_to_matrix,
)
from pytorch3d.renderer.cameras import CamerasBase, PerspectiveCameras
def pose_encoding_to_camera(
pose_encoding,
pose_encoding_type="absT_quaR_logFL",
log_focal_length_bias=1.8,
min_focal_length=0.1,
max_focal_length=20,
):
"""
Args:
pose_encoding: A tensor of shape `BxNxC`, containing a batch of
`BxN` `C`-dimensional pose encodings.
pose_encoding_type: The type of pose encoding,
only "absT_quaR_logFL" is supported.
"""
batch_size, num_poses, _ = pose_encoding.shape
pose_encoding_reshaped = pose_encoding.reshape(
-1, pose_encoding.shape[-1]
) # Reshape to BNxC
if pose_encoding_type == "absT_quaR_logFL":
# forced that 3 for absT, 4 for quaR, 2 logFL
# TODO: converted to 1 dim for logFL, consistent with our paper
abs_T = pose_encoding_reshaped[:, :3]
quaternion_R = pose_encoding_reshaped[:, 3:7]
R = quaternion_to_matrix(quaternion_R)
log_focal_length = pose_encoding_reshaped[:, 7:9]
# log_focal_length_bias was the hyperparameter
# to ensure the mean of logFL close to 0 during training
# Now converted back
focal_length = (log_focal_length + log_focal_length_bias).exp()
# clamp to avoid weird fl values
focal_length = torch.clamp(
focal_length, min=min_focal_length, max=max_focal_length
)
else:
raise ValueError(f"Unknown pose encoding {pose_encoding_type}")
pred_cameras = PerspectiveCameras(
focal_length=focal_length,
R=R,
T=abs_T,
device=R.device,
)
return pred_cameras
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