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 ...

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]