[Paper Review] Take-A-Photo: 3D-to-2D Generative Pre-training of Point Cloud Models

Ziyi Wang, Xumin Yu+3 more|arXiv (Cornell University)|Jul 27, 2023

3D Shape Modeling and AnalysisEngineering3 citations

TL;DR

This paper proposes TAP, a 3D-to-2D generative pre-training method that enhances point cloud models by generating view images from instructed camera poses using cross-attention mechanisms. By leveraging precise 2D image supervision instead of indirect point cloud reconstruction, TAP improves geometric and stereoscopic understanding, achieving state-of-the-art performance on ScanObjectNN and ShapeNetPart without pre-trained vision or language models.

ABSTRACT

With the overwhelming trend of mask image modeling led by MAE, generative pre-training has shown a remarkable potential to boost the performance of fundamental models in 2D vision. However, in 3D vision, the over-reliance on Transformer-based backbones and the unordered nature of point clouds have restricted the further development of generative pre-training. In this paper, we propose a novel 3D-to-2D generative pre-training method that is adaptable to any point cloud model. We propose to generate view images from different instructed poses via the cross-attention mechanism as the pre-training scheme. Generating view images has more precise supervision than its point cloud counterpart, thus assisting 3D backbones to have a finer comprehension of the geometrical structure and stereoscopic relations of the point cloud. Experimental results have proved the superiority of our proposed 3D-to-2D generative pre-training over previous pre-training methods. Our method is also effective in boosting the performance of architecture-oriented approaches, achieving state-of-the-art performance when fine-tuning on ScanObjectNN classification and ShapeNetPart segmentation tasks. Code is available at https://github.com/wangzy22/TAP.

Motivation & Objective

• To address the lack of precise supervision in 3D generative pre-training, which typically relies on imprecise point cloud reconstruction losses like Chamfer Distance.
• To overcome the limitation of existing methods that are restricted to Transformer-based backbones and lack adaptability to diverse point cloud architectures.
• To improve 3D backbone models' understanding of geometric structure and stereoscopic relations through more informative, image-based supervision.
• To develop a flexible pre-training framework compatible with any point cloud model, not limited to specific architectures.

Proposed method

• A pose-dependent Photograph Module uses cross-attention to encode camera pose conditions into 3D features, transforming them into 2D view image features.
• The 2D generator decodes these pose-conditioned features into view images, which are supervised by rendered ground truth images from synthetic data.
• The query generator derives physical formulations for optical lines, using origin coordinates, normalized direction vectors, and positional embeddings to generate queries for cross-attention.
• The method avoids explicit projection by letting cross-attention layers learn the 3D-to-2D feature rearrangement end-to-end, improving representation learning.
• A memory builder with pad tokens is used to handle variable-sized feature grids, improving training stability and performance.
• The pre-training pipeline is compatible with any point cloud backbone, enabling broad architectural adaptation.

Experimental results

Research questions

• RQ1 Can 3D-to-2D generative pre-training with image-based supervision improve 3D point cloud representation learning more effectively than point cloud reconstruction?
• RQ2 Does using pose-conditioned cross-attention with mathematically derived queries lead to better geometric and spatial understanding in 3D models?
• RQ3 Can a pre-training method designed for 2D image generation be effectively adapted to work with arbitrary point cloud backbones?
• RQ4 How does the proposed method compare to state-of-the-art architecture-based models on downstream 3D vision tasks?

Key findings

• TAP achieves state-of-the-art performance on the ScanObjectNN object classification benchmark, outperforming previous generative pre-training methods based on Transformers.
• With PointMLP as the backbone, TAP achieves 88.5% accuracy on ScanObjectNN, surpassing prior methods that do not use pre-trained image or text models.
• On the ShapeNetPart part segmentation task, TAP achieves state-of-the-art results among methods without external vision or language pre-training.
• Ablation studies confirm that the cross-attention mechanism in the Photograph Module is essential, outperforming direct projection or self-attention baselines.
• The mathematical formulation of queries—including origin coordinates, direction vectors, and positional embeddings—proves critical, with each component contributing significantly to performance.
• The inclusion of pad tokens in the memory builder improves generalization, and increasing cross-attention layers boosts performance up to a point before overfitting occurs.