  Our MATE is the first Test-Time-Training (TTT) method designed for 3D data,
which makes deep networks trained for point cloud classification robust to
distribution shifts occurring in test data. Like existing TTT methods from the
2D image domain, MATE also leverages test data for adaptation. Its test-time
objective is that of a Masked Autoencoder: a large portion of each test point
cloud is removed before it is fed to the network, tasked with reconstructing
the full point cloud. Once the network is updated, it is used to classify the
point cloud. We test MATE on several 3D object classification datasets and show
that it significantly improves robustness of deep networks to several types of
corruptions commonly occurring in 3D point clouds. We show that MATE is very
efficient in terms of the fraction of points it needs for the adaptation. It
can effectively adapt given as few as 5% of tokens of each test sample, making
it extremely lightweight. Our experiments show that MATE also achieves
competitive performance by adapting sparsely on the test data, which further
reduces its computational overhead, making it ideal for real-time applications.
