P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting (2208.02812v2)

Abstract: Nowadays, pre-training big models on large-scale datasets has become a crucial topic in deep learning. The pre-trained models with high representation ability and transferability achieve a great success and dominate many downstream tasks in natural language processing and 2D vision. However, it is non-trivial to promote such a pretraining-tuning paradigm to the 3D vision, given the limited training data that are relatively inconvenient to collect. In this paper, we provide a new perspective of leveraging pre-trained 2D knowledge in 3D domain to tackle this problem, tuning pre-trained image models with the novel Point-to-Pixel prompting for point cloud analysis at a minor parameter cost. Following the principle of prompting engineering, we transform point clouds into colorful images with geometry-preserved projection and geometry-aware coloring to adapt to pre-trained image models, whose weights are kept frozen during the end-to-end optimization of point cloud analysis tasks. We conduct extensive experiments to demonstrate that cooperating with our proposed Point-to-Pixel Prompting, better pre-trained image model will lead to consistently better performance in 3D vision. Enjoying prosperous development from image pre-training field, our method attains 89.3% accuracy on the hardest setting of ScanObjectNN, surpassing conventional point cloud models with much fewer trainable parameters. Our framework also exhibits very competitive performance on ModelNet classification and ShapeNet Part Segmentation. Code is available at https://github.com/wangzy22/P2P.

Tuning Pre-trained Image Models for 3D Point Cloud Analysis: An Evaluation of P2P

The paper "P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting" tackles the challenge of applying pre-trained 2D image models to 3D vision tasks, specifically point cloud analysis. The research introduces a novel framework called Point-to-Pixel Prompting (P2P), which utilizes pre-trained 2D models to enhance point cloud analysis by transforming 3D data into 2D representations suitable for 2D trained models.

The paper is premised on the difficulty of applying traditional pre-training and fine-tuning paradigms from 2D vision and NLP to 3D vision due to the scarcity of labeled 3D data. Here, the authors propose leveraging pre-trained 2D model knowledge via a new prompting strategy, Point-to-Pixel, which maintains geometry integrity through a process named geometry-preserved projection and enriches 2D representations with geometry-aware coloring. This transforms the colorless 3D point clouds into colorful 2D images that can be effectively processed by 2D pre-trained image models, whose weights remain fixed during this transformation.

The performance of the proposed P2P framework is validated using extensive experiments on benchmark datasets such as ScanObjectNN, ModelNet40, and ShapeNet Part Segmentation. Notably, the method achieves an accuracy of 89.3% on the most challenging configuration of ScanObjectNN, indicating a noteworthy performance with considerably fewer trainable parameters compared to traditional point cloud models.

Strong Numerical Results and Key Claims

• ScanObjectNN Performance: The framework achieved an accuracy of 89.3%, suggesting its potential in handling complex 3D tasks with a reduced parameter count.
• Efficiency in Parameter Utilization: The classification results demonstrate that with Point-to-Pixel Prompting, the increase in image model scale corresponds to higher classification performance, aligning with trends seen in traditional 2D applications.
• Part Segmentation Competitiveness: P2P also shows competitive performance in dense tasks like part segmentation, suggesting versatility across different types of 3D vision tasks.

Implications and Future Developments

Practically, this research introduces a new dimension of efficiency and efficacy in resource-constrained environments where 3D data is limited. By utilizing vast 2D knowledge bases, this method brings an alternative to costly 3D data generation and annotation. Theoretically, it suggests a promising direction in merging 2D and 3D information processing paradigms, potentially leading to more advanced cross-modality models.

The concept of transferring knowledge across dimensions has broader implications in AI, particularly for tasks involving multi-dimensional data such as in the fields of autonomous driving, robotics, and virtual/augmented reality. The paper implies a potential trajectory toward elaborating the P2P framework by incorporating advancements in differentiable rendering and exploring more sophisticated multi-view aggregation techniques without compromising computational efficiency.

In future explorations, applications of P2P could extend beyond point cloud classification and segmentation to tasks like 3D reconstruction or dynamic scene understanding, thereby broadening its utility. As 3D applications become more prevalent, refining this framework to accommodate dynamic scenes or integrate real-time processing capabilities could greatly enhance its applicability in real-world scenarios.

In conclusion, P2P provides compelling evidence for the integration of 2D pre-trained models into 3D vision tasks, nurturing a bridge between different visual perceptions and offering a robust solution to the data scarcity problem in 3D domains. The direction laid out by this research invites further exploration into the seamless unification of multi-dimensional learning and the enhancement of 3D vision through innovative engineering principles.