Function rotation_6d_to_matrix

utils/general_util.py:293–314

Converts 6D rotation representation by Zhou et al. [1] to rotation matrix using Gram--Schmidt orthogonalization per Section B of [1]. Args: d6: 6D rotation representation, of size (*, 6) Returns: batch of rotation matrices of size (*, 3, 3) [1] Zhou, Y., Barnes

(d6: torch.Tensor)

# https://github.com/facebookresearch/pytorch3d/blob/main/pytorch3d/transforms/rotation_conversions.py
def rotation_6d_to_matrix(d6: torch.Tensor) -> torch.Tensor:
    """
    Converts 6D rotation representation by Zhou et al. [1] to rotation matrix
    using Gram--Schmidt orthogonalization per Section B of [1].
    Args:
        d6: 6D rotation representation, of size (*, 6)

    Returns:
        batch of rotation matrices of size (*, 3, 3)

    [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H.
    On the Continuity of Rotation Representations in Neural Networks.
    IEEE Conference on Computer Vision and Pattern Recognition, 2019.
    Retrieved from http://arxiv.org/abs/1812.07035
    """

    a1, a2 = d6[..., :3], d6[..., 3:]
    b1 = F.normalize(a1, dim=-1)
    b2 = a2 - (b1 * a2).sum(-1, keepdim=True) * b1
    b2 = F.normalize(b2, dim=-1)
    b3 = torch.cross(b1, b2, dim=-1)
    return torch.stack((b1, b2, b3), dim=-2)


def strands_from_signal_torch(signal,norm='backward'):