Mitigating Positional Leakage in 3D Masked Autoencoders for Robust Representation Learning

Masked autoencoding has emerged as a prominent paradigmfor self-supervised learning on 3D point clouds, achieving competitiveperformance across downstream tasks. Unlike its 2D counterpart, 3Dmasked autoencoding directly reconstructs spatial coordinates, makingit inherently susceptible to positional leakage. In this work, we identifythat the decoder in existing 3D MAE frameworks tends to over-rely onpositional information, which weakens semantic representation learningand leads to suboptimal feature quality. To address this issue, we proposeMPL-MAE, a masked point learning framework that mitigates positionalover-reliance while enhancing the utilization of encoder features. Specifi-cally, we introduce a recalibrated positional embedding module that sup-presses metric-dominant coordinate signals while preserving geometrictopology, together with a gated positional interface module that dynam-ically regulates positional injection during reconstruction. These designspromote a more balanced interaction between spatial priors and seman-tic features, yielding robust and informative representations. Extensiveexperiments across downstream tasks demonstrate that MPL-MAE con-sistently achieves competitive performance, validating its effectiveness.Code is available at https://github.com/yanx57/MPL-MAE.