以下文章来源于3D视觉之心 ，作者3D视觉之心 3D视觉之心 . 3D视觉与SLAM、点云相关内容分享 点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近30个 方向 学习 路线 7DGS 炸场：一秒点燃动态世界！真实感实时渲染首次"七维全开" 具有复杂视角相关效果的真实感动态场景渲染在计算机视觉与图形学中仍然具有挑战性。示例包括来自真实 CT 扫描的动态心跳可视化以及日照周期中伴随吸收与散 射效应的云层过渡。合成动态场景的新视角对于虚拟现实、增强现实、内容创作与数字孪生等众多应用至关重要。尽管在静态场景重建与渲染方面，通过神经辐射场 （NeRF）以及最近的 3D 高斯溅射（3DGS）已取得显著进展，但实现 高质量、实时的具有视角相关效果的动态场景渲染仍面临巨大的计算与表征挑战 。 核心难点在于同时建模三个基本方面： 1) 空间几何， 2) 时间动态， 3) 视角相关外观 。每个维度都带来独特挑战。空间建模必须捕捉不同尺度下复杂的场景几何；时 间建模必须表示刚性与非刚性运动，可能涉及复杂形变；视角相关建模需要捕捉复杂的光传输效应，如散射、各向异性反射与半透明性。当同时考虑时，由于它们 ...

Core Viewpoint - The article discusses the development of Gaussian-LIC2, a novel LiDAR-Inertial-Camera 3D Gaussian splatting SLAM system that emphasizes visual quality, geometric accuracy, and real-time performance, addressing challenges in existing systems [52]. Group 1: SLAM Technology Overview - Simultaneous Localization and Mapping (SLAM) is a foundational technology for mixed reality systems and robotic applications, with recent advancements in neural radiance fields (NeRF) and 3D Gaussian splatting (3DGS) leading to a new paradigm in SLAM [3]. - The introduction of 3DGS has improved rendering speed and visual quality, making it more suitable for real-time applications compared to NeRF systems, although challenges remain in outdoor environments [4][6]. Group 2: Challenges in Existing Systems - Current methods often rely on high-density LiDAR data, which can lead to reconstruction issues in LiDAR blind spots or with sparse LiDAR [7]. - There is a tendency to prioritize visual quality over geometric accuracy, which limits the application of SLAM systems in tasks requiring precise geometry, such as obstacle avoidance [7]. - Existing systems primarily focus on rendering quality from trained viewpoints, neglecting the evaluation of new viewpoint synthesis capabilities [7]. Group 3: Gaussian-LIC2 System Contributions - Gaussian-LIC2 is designed to achieve robust and accurate pose estimation while constructing high-fidelity, geometrically accurate 3D Gaussian maps in real-time [8]. - The system consists of two main modules: a tightly coupled LiDAR-Inertial-Camera odometry and a progressive realistic mapping backend based on 3D Gaussian splatting [9]. - It effectively integrates LiDAR, IMU, and camera measurements to enhance odometry stability and accuracy in degraded scenarios [52]. Group 4: Depth Completion and Initialization - To address reconstruction blind spots caused by sparse LiDAR, Gaussian-LIC2 employs an efficient depth completion model that enhances Gaussian initialization coverage [12]. - The system utilizes a sparse depth completion network (SPNet) to predict dense depth maps from sparse LiDAR data and RGB images, achieving robust depth recovery in large-scale environments [31][32]. Group 5: Performance and Evaluation - Extensive experiments on public and self-collected datasets demonstrate the system's superior performance in localization accuracy, novel viewpoint synthesis quality, and real-time capabilities across various LiDAR types [52]. - The system achieves a significant reduction in drift error and maintains high rendering quality, showcasing its potential for practical applications in robotics and augmented reality [47][52].