Papers › Take-A-Photo: 3D-to-2D Generative Pre-training of Point Cloud Models
Take-A-Photo: 3D-to-2D Generative Pre-training of Point Cloud Models
Ziyi Wang, Xumin Yu, Yongming Rao, Jie zhou, Jiwen Lu
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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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Part Segmentation | ShapeNet-Part | PointMLP+TAP | Class Average IoU | 85.2 | #9 of 67 | Archive leaderboard | report |
| 3D Part Segmentation | ShapeNet-Part | PointMLP+TAP | Instance Average IoU | 86.9 | #9 of 67 | Archive leaderboard | report |
| 3D Point Cloud Classification | ScanObjectNN | PointMLP+TAP | Overall Accuracy | 88.5 | #36 of 77 | Archive leaderboard | report |
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Methods
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