Unsupervised Pose Decoder: Learn to Disentangle the Pose Attribute for Point Cloud Shape Analysis

Pose is a fundamental attribute of 3-D point cloud shape, which profoundly impacts point cloud analysis tasks. However, it is very tricky to directly solve the pose attribute, since it is deeply coupled with geometry shape. To this end, the representation separation strategy has been proposed, where the global representation is modeled as a combination of the pose-related part representation and the geometry shape part representation. However, these methods still cannot model the representation of the pose attribute well. As a reply, we design a new pose decoder in this article, learning to disentangle the pose attribute by exploiting its complement, i.e., the geometry shape part representation. Specifically, a Siamese structure is introduced constituting of two shared branches, where two consistent point clouds with different pose attributes are input. The geometry shape part representation and the global representation are learned in each branch network to solve the pose-related part representation for disentangling the pose distribution. Then, we emphasize the completeness and no redundancy of geometry shape part representation by designing two constraints: 1) we recover the learned geometry shape part representation to a point cloud and enforce it to maintain the same geometry shape as the original input point cloud to guarantee all geometry shape information is retained and 2) we develop two geometry shape part representations embedded from two branches to be the same so as to filter the pose information out. These two constraints are incorporated into the unsupervised loss function to train our pose decoder. Our pose decoder can be integrated into different point cloud shape analysis methods. We evaluate our pose decoder in point cloud classification and part segmentation tasks to handle the pose diversity problem of the input point cloud, which significantly improves the robustness. Besides, the obtained respective poses of input point clouds can be used to register them naturally, making the unsupervised method achieving superior performance.