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Running on Zero
Running on Zero
| # Modified for SplatAnything: inference-only release. | |
| import torch | |
| from .rotation import quat_to_rotmat, rotmat_to_quat | |
| def extrinsics_to_vector(ext): | |
| """Convert extrinsics to [t, q] vector.""" | |
| R = ext[..., :3, :3] | |
| t = ext[..., :3, 3] | |
| q = rotmat_to_quat(R) | |
| vec = torch.stack([t[..., 0], t[..., 1], t[..., 2], q[..., 0], q[..., 1], q[..., 2], q[..., 3]], dim=-1).float() | |
| return vec | |
| def vector_to_camera_matrices(cam_vec, image_hw=None, build_intr=True): | |
| """Reconstruct extrinsic and intrinsic matrix from vector.""" | |
| intr = None | |
| t = cam_vec[..., 0:3] | |
| q = cam_vec[..., 3:7] | |
| fov_v = cam_vec[..., 7] | |
| fov_u = cam_vec[..., 8] | |
| R = quat_to_rotmat(q) | |
| ext = torch.cat([R, t.unsqueeze(-1)], dim=-1) | |
| if build_intr: | |
| (h, w) = image_hw | |
| fy = h * 0.5 / torch.tan(fov_v * 0.5) | |
| fx = w * 0.5 / torch.tan(fov_u * 0.5) | |
| shape = cam_vec.shape[:-1] + (3, 3) | |
| intr = torch.zeros(shape, device=cam_vec.device, dtype=cam_vec.dtype) | |
| intr[..., 0, 0] = fx | |
| intr[..., 1, 1] = fy | |
| intr[..., 0, 2] = w * 0.5 | |
| intr[..., 1, 2] = h * 0.5 | |
| intr[..., 2, 2] = 1.0 | |
| return (ext, intr) | |