  Despite the tremendous progress of Masked Autoencoders (MAE) in developing
vision tasks such as image and video, exploring MAE in large-scale 3D point
clouds remains challenging due to the inherent irregularity. In contrast to
previous 3D MAE frameworks, which either design a complex decoder to infer
masked information from maintained regions or adopt sophisticated masking
strategies, we instead propose a much simpler paradigm. The core idea is to
apply a \textbf{G}enerative \textbf{D}ecoder for MAE (GD-MAE) to automatically
merges the surrounding context to restore the masked geometric knowledge in a
hierarchical fusion manner. In doing so, our approach is free from introducing
the heuristic design of decoders and enjoys the flexibility of exploring
various masking strategies. The corresponding part costs less than
\textbf{12\%} latency compared with conventional methods, while achieving
better performance. We demonstrate the efficacy of the proposed method on
several large-scale benchmarks: Waymo, KITTI, and ONCE. Consistent improvement
on downstream detection tasks illustrates strong robustness and generalization
capability. Not only our method reveals state-of-the-art results, but
remarkably, we achieve comparable accuracy even with \textbf{20\%} of the
labeled data on the Waymo dataset. Code will be released at
https://github.com/Nightmare-n/GD-MAE.
