Masked autoencoders (MAE) have recently been introduced to 3D self-supervised
pretraining for point clouds due to their great success in NLP and computer
vision. Unlike MAEs used in the image domain, where the pretext task is to
restore features at the masked pixels, such as colors, the existing 3D MAE
works reconstruct the missing geometry only, i.e, the location of the masked
points. In contrast to previous studies, we advocate that point location
recovery is inessential and restoring intrinsic point features is much
superior. To this end, we propose to ignore point position reconstruction and
recover high-order features at masked points including surface normals and
surface variations, through a novel attention-based decoder which is
independent of the encoder design. We validate the effectiveness of our pretext
task and decoder design using different encoder structures for 3D training and
demonstrate the advantages of our pretrained networks on various point cloud
analysis tasks.

通过自编码器预训练并采用使用基于注意力机制的解码器，本文在点云分析任务中表现优异，提高了点位置重建的不必要性，并还原了遮挡点的基础功能特征，包括表面法线和表面变化。