The proposed self-supervised learning framework can be extended to learn rotation-invariant features for other types of 3D data, such as voxel grids or polygon meshes, by adapting the data augmentation and feature extraction techniques.
For voxel grids, the framework can sample 3D regions from the voxel grid data and normalize the rotations within these regions to create rotation-invariant features. The tokenization process can be modified to work with voxel data, where each token represents a region in the voxel grid. The self-attention mechanism can then be applied to refine and aggregate these voxel-based tokens to extract rotation-invariant features.
When it comes to polygon meshes, the framework can first convert the mesh data into a point cloud representation. Similar to the oriented 3D point sets, the orientation vectors for the points in the mesh can be estimated using PCA or other methods. The RI-Tokenizer can then sample regions from the mesh data, normalize rotations, and extract features. The TS-Transformer can refine these features using self-attention and aggregate them to obtain rotation-invariant features for the polygon meshes.
By adapting the tokenization, normalization, and feature extraction processes to suit the specific characteristics of voxel grids and polygon meshes, the self-supervised learning framework can effectively learn rotation-invariant features for these types of 3D data.