Depth-Regularized Optimization for 3D Gaussian Splatting in Few-Shot Images

• URL: http://arxiv.org/abs/2311.13398v3
• Date: Thu, 4 Jan 2024 08:19:16 GMT
• Title: Depth-Regularized Optimization for 3D Gaussian Splatting in Few-Shot Images
• Authors: Jaeyoung Chung, Jeongtaek Oh, and Kyoung Mu Lee
• Abstract summary: We introduce a dense depth map as a geometry guide to mitigate overfitting. The adjusted depth aids in the color-based optimization of 3D Gaussian splatting. We verify the proposed method on the NeRF-LLFF dataset with varying numbers of few images.
• Score: 47.14713579719103
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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