6D rotation representation ("On the Continuity of Rotation Representations in Neural Networks") for tensorflow.
This code is implemmented and tested with tensorflow 1.11.0.
I didn't use any spetial operator, so it should also work for other version of tensorflow.
Just add the tf_rotation6d_to_matrix after your output, whose last dimension of tensor should be 6.
"""
Any model output whose last dimension is 6.
e.g. output = tf.layers.dense(hidden, 6)
"""
rot = tf_rotation6d_to_matrix(output)
I very simple example of transformation between 6D continuous representation and SO(3) can be found in example.py
Here I crop some parts of the context from the paper, FYI.
According to the Section 3 and 4 of the paper, the target transformation between continuous representation and SO(n) can be formulate as follows. It's derived based on a Gram-Schmidt process.
If you found it looks a little bit complicated, you can directly go to Appendix B. There is a very simple formulation of the 6D and SO(3).
Besides, according to the features of rotation matrix, the formulation can be quite concise. You can found the concise transformation between 6D and SO(3) in the source code.
Code work by Jia-Yau Shiau jiayau.shiau@gmail.com.