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pypose.rxso3

pypose.rxso3 = functools.partial(<class 'pypose.lietensor.lietensor.LieTensor'>, ltype=<pypose.lietensor.lietensor.rxso3Type object>)

Alias of rxso3 type LieTensor.

Parameters:

data (Tensor, or list, or ‘int…’) –

A Tensor object, or constructing a Tensor object from list, which defines tensor data (see below), or from ‘int…’, which defines tensor shape.

The shape of Tensor object must be (*, 4), where * is empty, one, or more batched dimensions (the lshape of this LieTensor), otherwise error will be raised.

Internally, rxso3 LieTensors are stored by concatenating the axis-angle representation of the rotation with the log scale:

\[\mathrm{data}[*, :] = [\delta_x, \delta_y, \delta_z, \log s], \]

where \(\begin{pmatrix} \delta_x & \delta_y & \delta_z \end{pmatrix}^T\) is the axis-angle vector in pypose.so3, and \(s \in \mathbb{R}\) is the scaling factor in pypose.RxSO3.

Examples

>>> pp.rxso3(torch.randn(2, 4))
rxso3Type LieTensor:
tensor([[ 0.3752, -0.1576,  1.2057,  0.6086],
        [ 0.8434,  0.2449,  0.0488, -0.1202]])
>>> pp.rxso3([0, 0, 0, 0, 1])
RxSO3Type LieTensor:
tensor([0., 0., 0., 0., 1.])

If data is tensor-like, the last dimension should correspond to the 4 elements of the above embedding.

Note

It is not advised to construct rxso3 Tensors by specifying storage sizes with ‘int…’, which does not initialize data.

Consider using pypose.randn_rxso3 or pypose.identity_rxso3 instead.

See pypose.Exp, pypose.Inv for implementations of relevant operations.