Learning 3D representation plays a critical role in masked autoencoder (MAE)
based pre-training methods for point cloud, including single-modal and
cross-modal based MAE. Specifically, although cross-modal MAE methods learn
strong 3D representations via the auxiliary of other modal knowledge, they
often suffer from heavy computational burdens and heavily rely on massive
cross-modal data pairs that are often unavailable, which hinders their
applications in practice. Instead, single-modal methods with solely point
clouds as input are preferred in real applications due to their simplicity and
efficiency. However, such methods easily suffer from limited 3D representations
with global random mask input. To learn compact 3D representations, we propose
a simple yet effective Point Feature Enhancement Masked Autoencoders
(Point-FEMAE), which mainly consists of a global branch and a local branch to
capture latent semantic features. Specifically, to learn more compact features,
a share-parameter Transformer encoder is introduced to extract point features
from the global and local unmasked patches obtained by global random and local
block mask strategies, followed by a specific decoder to reconstruct.
Meanwhile, to further enhance features in the local branch, we propose a Local
Enhancement Module with local patch convolution to perceive fine-grained local
context at larger scales. Our method significantly improves the pre-training
efficiency compared to cross-modal alternatives, and extensive downstream
experiments underscore the state-of-the-art effectiveness, particularly
outperforming our baseline (Point-MAE) by 5.16%, 5.00%, and 5.04% in three
variants of ScanObjectNN, respectively. The code is available at
this https URL

为了学习紧凑的 3D 表示，我们提出了一个简单而有效的 Point Feature Enhancement Masked Autoencoders (Point-FEMAE)，它主要由一个全局分支和一个局部分支组成，通过全局随机和局部块掩码策略获得的全局和局部未经掩码的补丁提取点特征，然后使用特定的解码器进行重构，同时，为了进一步增强局部分支中的特征，我们提出了一个局部增强模块，使用局部补丁卷积方法感知更大尺度上的细粒度局部上下文。