Feature Extraction and Representation of Pre-Training Point Cloud Based on Diffusion Models

The pretrain-finetune paradigm of pre-training a model on large amounts of image and text data and then fine-tuning the model for a specific task has led to significant progress in many 2D image and natural language processing tasks. Similarly, the use of pre-training methods in point cloud data can also enhance the working performance and generalization ability of the model. Therefore, in this paper, we propose a pre-training framework based on a diffusion model called PreDifPoint. It is able to accomplish the pretraining of the model's backbone network through a diffusion process of gradual denoising. We aggregate the potential features extracted from the backbone network, input them as conditions into the subsequent diffusion model, and direct the point-to-point mapping relationship of the noisy point clouds at neighboring time steps, so as to generate high-quality point clouds and at the same time better perform various downstream tasks of the point clouds. We also introduce a bi-directional covariate attention (DXCA-Attention) mechanism for capturing complex feature interactions, fusing local and global features, and improving the detail recovery of point clouds. In addition, we propose a density-adaptive sampling strategy, which can help the model dynamically adjust the sampling strategy between different time steps, and guide the model to pay more attention to the denser regions in the point cloud, thus improving the effectiveness of the model in point cloud recovery. Our PreDifPoint framework achieves more competitive results on various real-world datasets. Specifically, PreDifPoint achieves an overall accuracy of 87.96%, which is 0.35% higher than PointDif, on the classification task on PB-T50-395RS, a variant of ScanObjectNN dataset.