# MATE: Masked Autoencoders are Online 3D Test-Time Learners

Source: [MATE: Masked Autoencoders are Online 3D Test-Time Learners](<https://www.sota2.com/research/paper/68fa6ecf1e209131cea4250a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:07:49.028Z

Authors: M. Jehanzeb Mirza, Inkyu Shin, Wei Lin, Andreas Schriebl, Kunyang Sun, Jaesung Choe, Horst Possegger, Mateusz Kozinski, In So Kweon, Kun-Jin Yoon, Horst Bischof

Published: 2022-11-21T13:19:08.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [3D Point Cloud Classification on ShapeNet-C](<https://www.sota2.com/research/sota/3d-point-cloud-classification-on-shapenet-c.md>): 3D Point Cloud Classification; ShapeNet-C; Accuracy (Uniform Noise): 77.8.

- [3D Point Cloud Classification on ScanObjectNN-C 1.0 (test)](<https://www.sota2.com/research/sota/3d-point-cloud-classification-on-scanobjectnn-c-1-0-test.md>): 3D Point Cloud Classification; ScanObjectNN-C 1.0 (test); Accuracy (Uniform Noise): 27.5.

- [3D Point Cloud Classification on ModelNet40-C (test)](<https://www.sota2.com/research/sota/3d-point-cloud-classification-on-modelnet40-c-test.md>): 3D Point Cloud Classification; ModelNet40-C (test); Robustness to Rotation: 36.7.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2211.11432>)

[PDF](<https://arxiv.org/pdf/2211.11432.pdf>)