Understanding transformations and rotations
In 3D deep learning and computer vision, we usually need to work with 3D transformations, such as rotations and 3D rigid motions. PyTorch3D provides a high-level encapsulation of these transformations in its pytorch3d.transforms.Transform3d class. One advantage of the Transform3d class is that it is mini-batch based. Thus, as frequently needed in 3D deep learning, it is possible to apply a mini-batch of transformations on a mini-batch of meshes only within several lines of code. Another advantage of Transform3d is that gradient backpropagation can straightforwardly pass through Transform3d.
PyTorch3D also provides many lower-level APIs for computations in the Lie groups SO(3) and SE(3). Here, SO(3) denotes the special orthogonal group in 3D and SE(3) denotes the special Euclidean group in 3D. Informally speaking, SO(3) denotes the set of all the rotation transformations and SE(3) denotes the set of all the rigid transformations in 3D....
