Abstract
We propose a dense RGBD SLAM system based on 3D Gaussian Splatting that provides metrically accurate pose tracking and visually realistic reconstruction. To this end, we first propose a Gaussian densification strategy based on the rendering loss to map unobserved areas and refine reobserved areas. Second, we introduce extra regularization parameters to alleviate the forgetting problem in the continuous mapping problem, where parameters tend to overfit the latest frame and result in decreasing rendering quality for previous frames. Both mapping and tracking are performed with Gaussian parameters by minimizing re-rendering loss in a differentiable way. Compared to recent neural and concurrently developed gaussian splatting RGBD SLAM baselines, our method achieves state-of-the-art results on the synthetic dataset Replica and competitive results on the real-world dataset TUM.
Abstract (translated)
我们提出了一个基于3D高斯平铺的密集RGBD SLAM系统，该系统提供精确的 pose 跟踪和视觉上逼真的重建。为此，我们首先提出了一种基于渲染损失的高斯密度策略，将未观察到的区域映射到优化观察到的区域。其次，我们引入了一些额外的正则化参数来减轻连续映射问题中的遗忘问题，其中参数倾向于过拟合最新的帧，导致前几帧的渲染质量降低。通过最小化在可导方式下的重新渲染损失来进行映射和跟踪。与最近的神经网络和同时开发的Gaussian Splatting RGBD SLAM基线相比，我们的方法在合成数据集Replica上实现了最先进的结果，并在真实世界数据集TUM上实现了竞争力的结果。
URL
https://arxiv.org/abs/2403.12535
