Core Insights - The article introduces 7D Gaussian Splatting (7DGS), a novel framework for real-time rendering of dynamic scenes that unifies spatial, temporal, and angular dimensions into a single 7D Gaussian representation [2][44] - The method addresses the challenges of modeling complex visual effects related to perspective, time dynamics, and spatial geometry, which are crucial for applications in virtual reality, augmented reality, and digital twins [3][44] Technical Contributions - 7DGS models scene elements as 7D Gaussians, capturing the interdependencies between geometry, dynamics, and appearance, allowing for accurate modeling of phenomena like moving specular highlights and anisotropic reflections [3][10] - The framework includes an efficient conditional slicing mechanism that projects the high-dimensional Gaussian representation into a format compatible with existing real-time rendering processes, ensuring both efficiency and fidelity [10][38] - Experimental results demonstrate that 7DGS outperforms previous methods, achieving a peak signal-to-noise ratio (PSNR) improvement of up to 7.36 dB while maintaining rendering speeds exceeding 400 frames per second (FPS) [10][44] Methodology - The 7D Gaussian representation is defined to encode spatial, temporal, and directional attributes, allowing for a comprehensive modeling of complex dependencies across these dimensions [18][19] - The article details a conditional slicing mechanism that enables efficient integration of temporal dynamics and perspective effects into traditional 3D rendering workflows [23][31] - An adaptive Gaussian refinement technique is introduced to dynamically update Gaussian parameters, enhancing the representation of complex dynamic behaviors such as non-rigid deformations [32][36] Experimental Evaluation - The framework was evaluated across multiple datasets, including heart scans and dynamic cloud simulations, with metrics such as PSNR, structural similarity index (SSIM), and rendering speed reported [39][41] - Results indicate that 7DGS achieves superior image quality and efficiency compared to existing techniques, reinforcing its potential for advancing dynamic scene rendering in the industry [44]

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].