A Strong View-Free Baseline Approach for Single-View Image Guided Point Cloud Completion
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909944966545408 |
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| author | Lin, Fangzhou Dai, Zilin Sanku, Rigved Hou, Songlin Yamada, Kazunori D Zhang, Haichong K. Zhang, Ziming |
| author_facet | Lin, Fangzhou Dai, Zilin Sanku, Rigved Hou, Songlin Yamada, Kazunori D Zhang, Haichong K. Zhang, Ziming |
| contents | The single-view image guided point cloud completion (SVIPC) task aims to reconstruct a complete point cloud from a partial input with the help of a single-view image. While previous works have demonstrated the effectiveness of this multimodal approach, the fundamental necessity of image guidance remains largely unexamined. To explore this, we propose a strong baseline approach for SVIPC based on an attention-based multi-branch encoder-decoder network that only takes partial point clouds as input, view-free. Our hierarchical self-fusion mechanism, driven by cross-attention and self-attention layers, effectively integrates information across multiple streams, enriching feature representations and strengthening the networks ability to capture geometric structures. Extensive experiments and ablation studies on the ShapeNet-ViPC dataset demonstrate that our view-free framework performs superiorly to state-of-the-art SVIPC methods. We hope our findings provide new insights into the development of multimodal learning in SVIPC. Our demo code will be available at https://github.com/Zhang-VISLab. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_15747 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Strong View-Free Baseline Approach for Single-View Image Guided Point Cloud Completion Lin, Fangzhou Dai, Zilin Sanku, Rigved Hou, Songlin Yamada, Kazunori D Zhang, Haichong K. Zhang, Ziming Computer Vision and Pattern Recognition Image and Video Processing The single-view image guided point cloud completion (SVIPC) task aims to reconstruct a complete point cloud from a partial input with the help of a single-view image. While previous works have demonstrated the effectiveness of this multimodal approach, the fundamental necessity of image guidance remains largely unexamined. To explore this, we propose a strong baseline approach for SVIPC based on an attention-based multi-branch encoder-decoder network that only takes partial point clouds as input, view-free. Our hierarchical self-fusion mechanism, driven by cross-attention and self-attention layers, effectively integrates information across multiple streams, enriching feature representations and strengthening the networks ability to capture geometric structures. Extensive experiments and ablation studies on the ShapeNet-ViPC dataset demonstrate that our view-free framework performs superiorly to state-of-the-art SVIPC methods. We hope our findings provide new insights into the development of multimodal learning in SVIPC. Our demo code will be available at https://github.com/Zhang-VISLab. |
| title | A Strong View-Free Baseline Approach for Single-View Image Guided Point Cloud Completion |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2506.15747 |