RU4D-SLAM：面向4D场景重建的高斯溅射SLAM不确定性重加权
RU4D-SLAM: Reweighting Uncertainty in Gaussian Splatting SLAM for 4D Scene Reconstruction
AI总结：
提出RU4D-SLAM框架，通过引入时间因子、不确定性感知和语义引导重加权机制，解决动态环境中3D高斯溅射SLAM的跟踪与4D场景重建问题。
摘要：
将3D高斯溅射与同时定位与地图构建（SLAM）相结合的方法因其能够在运动过程中实现连续3D环境重建而受到广泛关注。然而，现有方法在动态环境中表现不佳，尤其是移动物体使3D重建复杂化，进而阻碍了可靠的跟踪。4D重建的出现，特别是4D高斯溅射，为解决这些挑战提供了有前景的方向，但其在4D感知SLAM中的潜力尚未得到充分探索。沿着这一方向，我们提出了一种鲁棒且高效的框架，即面向4D场景重建的高斯溅射SLAM不确定性重加权（RU4D-SLAM），该框架将时间因子引入空间3D表示，同时结合了场景变化的不确定性感知、模糊图像合成和动态场景重建。我们通过集成运动模糊渲染增强了动态场景表示，并通过扩展原本为静态场景设计的逐像素不确定性建模来处理模糊图像，从而改进了不确定性感知跟踪。此外，我们提出了一种用于动态场景中逐像素不确定性估计的语义引导重加权机制，并引入可学习的不透明度权重以支持自适应4D映射。在标准基准上的大量实验表明，我们的方法在轨迹精度和4D场景重建方面显著优于最先进的方法，尤其是在存在移动物体和低质量输入的动态环境中。代码地址：https://ru4d-slam.github.io
英文摘要：
Combining 3D Gaussian splatting with Simultaneous Localization and Mapping (SLAM) has gained popularity as it enables continuous 3D environment reconstruction during motion. However, existing methods struggle in dynamic environments, particularly moving objects complicate 3D reconstruction and, in turn, hinder reliable tracking. The emergence of 4D reconstruction, especially 4D Gaussian splatting, offers a promising direction for addressing these challenges, yet its potential for 4D-aware SLAM remains largely underexplored. Along this direction, we propose a robust and efficient framework, namely Reweighting Uncertainty in Gaussian Splatting SLAM (RU4D-SLAM) for 4D scene reconstruction, that introduces temporal factors into spatial 3D representation while incorporating uncertainty-aware perception of scene changes, blurred image synthesis, and dynamic scene reconstruction. We enhance dynamic scene representation by integrating motion blur rendering, and improve uncertainty-aware tracking by extending per-pixel uncertainty modeling, which is originally designed for static scenarios, to handle blurred images. Furthermore, we propose a semantic-guided reweighting mechanism for per-pixel uncertainty estimation in dynamic scenes, and introduce a learnable opacity weight to support adaptive 4D mapping. Extensive experiments on standard benchmarks demonstrate that our method substantially outperforms state-of-the-art approaches in both trajectory accuracy and 4D scene reconstruction, particularly in dynamic environments with moving objects and low-quality inputs. Code available: https://ru4d-slam.github.io
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。
