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]

Group 1 - The core challenge in robotics is joint target manipulation, which involves complex kinematic constraints and limited physical reasoning capabilities of existing methods [3][4] - The proposed ArtGS framework integrates 3D Gaussian Splatting (3DGS) with visual-physical modeling to enhance understanding and interaction with joint targets, ensuring physically consistent motion constraints [3][4][20] - ArtGS consists of three key modules: static Gaussian reconstruction, VLM-based skeletal inference, and dynamic 3D Gaussian joint modeling [4] Group 2 - Static 3D Gaussian reconstruction utilizes 3D Gaussian splatting to create high-fidelity 3D scenes from multi-view RGB-D images, representing the scene as a collection of 3D Gaussian spheres [5] - VLM-based skeletal inference employs a fine-tuned visual-language model (VLM) to estimate joint parameters, generating target views to assist in visual question answering [6][8] - Dynamic 3D Gaussian joint modeling implements impedance control for interaction with the environment, optimizing joint parameters through differential rendering [10] Group 3 - Experimental validation shows that ArtGS significantly outperforms baseline methods in joint parameter estimation, with lower angular error (AE) and origin error (OE) [12] - In simulation, ArtGS achieves a manipulation success rate ranging from 62.4% to 90.3%, which is substantially higher than other methods like TD3 and Where2Act [14] - Real-world experiments demonstrate a perfect success rate of 10/10 for drawer operations and 9/10 for cabinet operations, indicating the effectiveness of the optimized version of ArtGS [14][17] Group 4 - Ablation studies reveal that even with initial axis estimation errors exceeding 20°, ArtGS can still enhance operation success rates through 3DGS optimization [19] - ArtGS exhibits cross-embodiment adaptability, accurately reconstructing various robotic arms, particularly excelling in gripper rendering details [19][20] - The core contribution of ArtGS lies in transforming 3DGS into a visual-physical model for joint targets, ensuring spatiotemporal consistency in differentiable operation trajectories [20] Group 5 - Future directions for ArtGS include expanding capabilities to handle more complex scenarios and improving modeling and manipulation of multi-joint, high-dynamic targets [21]