KeyGS：一种面向单目图像序列的以关键帧为中心的高斯泼溅方法
KeyGS: A Keyframe-Centric Gaussian Splatting Method for Monocular Image Sequences
AI总结：
KeyGS提出一种以关键帧为中心的高斯泼溅方法，先用SfM快速获取粗略相机位姿，再结合频率感知致密化与联合优化进行细化，在将训练时间从小时级缩短到分钟级的同时提升新视角合成与位姿估计精度。
摘要：
从稀疏二维图像重建高质量三维模型在计算机视觉领域引起了广泛关注。近年来，三维高斯泼溅（3DGS）因其显式表示、高效的训练速度和实时渲染能力而备受瞩目。然而，现有方法在重建过程中仍然严重依赖准确的相机位姿。尽管最近一些方法尝试在单目视频数据集上不经过运动恢复结构（SfM）预处理来训练3DGS模型，但这些方法训练时间过长，导致在许多应用中难以实际使用。本文提出了一个无需任何深度模型或匹配模型的高效框架。我们的方法首先利用SfM在几秒钟内快速获得粗略的相机位姿，然后借助3DGS中的稠密表示对这些位姿进行优化。该框架有效解决了训练时间过长的问题。此外，我们将致密化过程与联合优化相结合，并提出了一种由粗到细的频率感知致密化方法，以重建不同层次的细节。该方法避免了相机位姿估计因高频信号而陷入局部最小值或发生漂移。与先前方法相比，我们的方法将训练时间从数小时显著缩短至数分钟，同时实现了更精确的新视角合成和相机位姿估计。
英文摘要：
Reconstructing high-quality 3D models from sparse 2D images has garnered significant attention in computer vision. Recently, 3D Gaussian Splatting (3DGS) has gained prominence due to its explicit representation with efficient training speed and real-time rendering capabilities. However, existing methods still heavily depend on accurate camera poses for reconstruction. Although some recent approaches attempt to train 3DGS models without the Structure-from-Motion (SfM) preprocessing from monocular video datasets, these methods suffer from prolonged training times, making them impractical for many applications. In this paper, we present an efficient framework that operates without any depth or matching model. Our approach initially uses SfM to quickly obtain rough camera poses within seconds, and then refines these poses by leveraging the dense representation in 3DGS. This framework effectively addresses the issue of long training times. Additionally, we integrate the densification process with joint refinement and propose a coarse-to-fine frequency-aware densification to reconstruct different levels of details. This approach prevents camera pose estimation from being trapped in local minima or drifting due to high-frequency signals. Our method significantly reduces training time from hours to minutes while achieving more accurate novel view synthesis and camera pose estimation compared to previous methods.
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