Point-to-Pixel Prompting for Point Cloud Analysis With Pre-Trained Image Models.

Nowadays, pre-training big models on large-scale datasets has achieved great success and dominated many downstream tasks in natural language processing and 2D vision, while pre-training in 3D vision is still under development. In this paper, we provide a new perspective of transferring the pre-trained knowledge from 2D domain to 3D domain with Point-to-Pixel Prompting in data space and Pixel-to-Point distillation in feature space, exploiting shared knowledge in images and point clouds that display the same visual world. Following the principle of prompting engineering, Point-to-Pixel Prompting transforms point clouds into colorful images with geometry-preserved projection and geometry-aware coloring. Then the pre-trained image models can be directly implemented for point cloud tasks without structural changes or weight modifications. With projection correspondence in feature space, Pixel-to-Point distillation further regards pre-trained image models as the teacher model and distills pre-trained 2D knowledge to student point cloud models, remarkably enhancing inference efficiency and model capacity for point cloud analysis. We conduct extensive experiments in both object classification and scene segmentation under various settings to demonstrate the superiority of our method. In object classification, we reveal the important scale-up trend of Point-to-Pixel Prompting and attain 90.3% accuracy on ScanObjectNN dataset, surpassing previous literature by a large margin. In scene-level semantic segmentation, our method outperforms traditional 3D analysis approaches and shows competitive capacity in dense prediction tasks.