Manifold clustering is an important problem in motion and video segmentation, natural image clustering, and other applications where high-dimensional data lie on multiple, low-dimensional, nonlinear manifolds.

The croissant metadata record is available at croissant. We chose GitHub and Google Drive respectively to store our code and dataset. Both are widely recognized as reliable data storage platforms, ensuring long-term preservation. We highly recommend downloading the raw data directly and following the provided instructions to simplify the data processing steps. Our dataset is structured as follows: the local directory contains client-specific data for local training, while all clients aggregates data from all clients for federated learning.

D.9 Parameter Size and Inference Speed . . . . . . . . . . . . . . . . . . . . . . . .

Built upon existing 3D scanning data, the resulting multi-modal 3D dataset encompasses 1.4M