One popular approach is to use a variant of the PointNet architecture, such as PointNet++, which uses a hierarchical neural network to process point clouds of varying sizes. This type of network takes unorganized point cloud data as input and uses a series of neural network layers to learn a representation of the point cloud that is invariant to permutations of the input points. This allows it to process point clouds of different sizes and shapes, and to estimate the 6DoF pose of the object represented by the point cloud.

Another approach is to use a hybrid architecture that combines deep learning with traditional computer vision techniques. An example of this is the DenseFusion network, which uses a convolutional neural network (CNN) to process 2D images, and a point cloud neural network (PCNN) to process 3D point clouds. This allows it to take advantage of the strengths of both 2D and 3D data for pose estimation, and to provide more accurate and stable estimates of the 6DoF pose.

Overall, there are many different network architectures that can be used for estimating 6DoF poses from point clouds, and the best choice will depend on the specific requirements of your application. I would recommend reading some of the recent research papers in this area to get a better understanding of the different approaches and their trade-offs.