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

• URL: http://arxiv.org/abs/2307.14971v2
• Date: Thu, 7 Sep 2023 05:44:37 GMT
• Title: Take-A-Photo: 3D-to-2D Generative Pre-training of Point Cloud Models
• Authors: Ziyi Wang, Xumin Yu, Yongming Rao, Jie Zhou, Jiwen Lu
• Abstract summary: generative pre-training can boost the performance of fundamental models in 2D vision. 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. We propose a novel 3D-to-2D generative pre-training method that is adaptable to any point cloud model.
• Score: 97.58685709663287
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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