少样本图像下3D高斯泼溅的深度正则化优化
Depth-Regularized Optimization for 3D Gaussian Splatting in Few-Shot Images
AI总结：
针对少样本图像下3D Gaussian splatting易过拟合的问题，提出利用预训练单目深度估计结合COLMAP特征点对齐生成的稠密深度图作为几何引导，有效缓解悬浮伪影并提升几何稳健性。
摘要：
本文提出一种在图像数量有限时优化Gaussian splatting以避免过拟合的方法。通过结合大量Gaussian splats表示3D场景已展现出卓越的视觉质量，但在仅有少量图像时往往容易过拟合训练视角。为解决此问题，我们引入稠密深度图作为几何引导来缓解过拟合。我们利用预训练的单目深度估计模型获取深度图，并使用稀疏COLMAP特征点对齐尺度与偏移。调整后的深度辅助基于颜色的3D Gaussian splatting优化，减少了悬浮伪影，并确保遵循几何约束。我们在NeRF-LLFF数据集上使用不同数量的少样本图像验证了该方法。与仅依赖图像的原始方法相比，我们的方法展现出稳健的几何特性。项目页面：robot0321.github.io/DepthRegGS
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
