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| Format: | Preprint |
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2024
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| Online-Zugang: | https://arxiv.org/abs/2403.10860 |
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| _version_ | 1866917945901318144 |
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| author | Wu, Junyang Gu, Yun Yang, Guang-Zhong |
| author_facet | Wu, Junyang Gu, Yun Yang, Guang-Zhong |
| contents | Robot assisted endoluminal intervention is an emerging technique for both benign and malignant luminal lesions. With vision-based navigation, when combined with pre-operative imaging data as priors, it is possible to recover position and pose of the endoscope without the need of additional sensors. In practice, however, aligning pre-operative and intra-operative domains is complicated by significant texture differences. Although methods such as style transfer can be used to address this issue, they require large datasets from both source and target domains with prolonged training times. This paper proposes an efficient domain transfer method based on stylized Gaussian splatting, only requiring a few of real images (10 images) with very fast training time. Specifically, the transfer process includes two phases. In the first phase, the 3D models reconstructed from CT scans are represented as differential Gaussian point clouds. In the second phase, only color appearance related parameters are optimized to transfer the style and preserve the visual content. A novel structure consistency loss is applied to latent features and depth levels to enhance the stability of the transferred images. Detailed validation was performed to demonstrate the performance advantages of the proposed method compared to that of the current state-of-the-art, highlighting the potential for intra-operative surgical navigation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_10860 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sim2Real within 5 Minutes: Efficient Domain Transfer with Stylized Gaussian Splatting for Endoscopic Images Wu, Junyang Gu, Yun Yang, Guang-Zhong Computer Vision and Pattern Recognition Artificial Intelligence Robot assisted endoluminal intervention is an emerging technique for both benign and malignant luminal lesions. With vision-based navigation, when combined with pre-operative imaging data as priors, it is possible to recover position and pose of the endoscope without the need of additional sensors. In practice, however, aligning pre-operative and intra-operative domains is complicated by significant texture differences. Although methods such as style transfer can be used to address this issue, they require large datasets from both source and target domains with prolonged training times. This paper proposes an efficient domain transfer method based on stylized Gaussian splatting, only requiring a few of real images (10 images) with very fast training time. Specifically, the transfer process includes two phases. In the first phase, the 3D models reconstructed from CT scans are represented as differential Gaussian point clouds. In the second phase, only color appearance related parameters are optimized to transfer the style and preserve the visual content. A novel structure consistency loss is applied to latent features and depth levels to enhance the stability of the transferred images. Detailed validation was performed to demonstrate the performance advantages of the proposed method compared to that of the current state-of-the-art, highlighting the potential for intra-operative surgical navigation. |
| title | Sim2Real within 5 Minutes: Efficient Domain Transfer with Stylized Gaussian Splatting for Endoscopic Images |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2403.10860 |