Depth-Regularized Optimization for 3D Gaussian Splatting in Few-Shot Images
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866913185142931456 |
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| author | Chung, Jaeyoung Oh, Jeongtaek Lee, Kyoung Mu |
| author_facet | Chung, Jaeyoung Oh, Jeongtaek Lee, Kyoung Mu |
| contents | In this paper, we present a method to optimize Gaussian splatting with a limited number of images while avoiding overfitting. Representing a 3D scene by combining numerous Gaussian splats has yielded outstanding visual quality. However, it tends to overfit the training views when only a small number of images are available. To address this issue, we introduce a dense depth map as a geometry guide to mitigate overfitting. We obtained the depth map using a pre-trained monocular depth estimation model and aligning the scale and offset using sparse COLMAP feature points. The adjusted depth aids in the color-based optimization of 3D Gaussian splatting, mitigating floating artifacts, and ensuring adherence to geometric constraints. We verify the proposed method on the NeRF-LLFF dataset with varying numbers of few images. Our approach demonstrates robust geometry compared to the original method that relies solely on images. Project page: robot0321.github.io/DepthRegGS |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_13398 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Depth-Regularized Optimization for 3D Gaussian Splatting in Few-Shot Images Chung, Jaeyoung Oh, Jeongtaek Lee, Kyoung Mu Computer Vision and Pattern Recognition Graphics In this paper, we present a method to optimize Gaussian splatting with a limited number of images while avoiding overfitting. Representing a 3D scene by combining numerous Gaussian splats has yielded outstanding visual quality. However, it tends to overfit the training views when only a small number of images are available. To address this issue, we introduce a dense depth map as a geometry guide to mitigate overfitting. We obtained the depth map using a pre-trained monocular depth estimation model and aligning the scale and offset using sparse COLMAP feature points. The adjusted depth aids in the color-based optimization of 3D Gaussian splatting, mitigating floating artifacts, and ensuring adherence to geometric constraints. We verify the proposed method on the NeRF-LLFF dataset with varying numbers of few images. Our approach demonstrates robust geometry compared to the original method that relies solely on images. Project page: robot0321.github.io/DepthRegGS |
| title | Depth-Regularized Optimization for 3D Gaussian Splatting in Few-Shot Images |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2311.13398 |