Self-supervised representation learning (SSRL) has gained increasing
attention in point cloud understanding, in addressing the challenges posed by
3D data scarcity and high annotation costs. This paper presents PCExpert, a
novel SSRL approach that reinterprets point clouds as "specialized images".
This conceptual shift allows PCExpert to leverage knowledge derived from
large-scale image modality in a more direct and deeper manner, via extensively
sharing the parameters with a pre-trained image encoder in a multi-way
Transformer architecture. The parameter sharing strategy, combined with a novel
pretext task for pre-training, i.e., transformation estimation, empowers
PCExpert to outperform the state of the arts in a variety of tasks, with a
remarkable reduction in the number of trainable parameters. Notably, PCExpert's
performance under LINEAR fine-tuning (e.g., yielding a 90.02% overall accuracy
on ScanObjectNN) has already approached the results obtained with FULL model
fine-tuning (92.66%), demonstrating its effective and robust representation
capability.

PCExpert 是一种基于自监督表示学习的新方法，将点云重新解释为 ' 特殊图像 '，通过与预训练图像编码器在多路 Transformer 架构中广泛共享参数的策略，结合用于预训练的新颖预文本任务，即变换估计，使其在各种任务中表现优异并大幅减少可训练参数的数量。