4D神经体素溅射：基于体素化的高斯溅射的动态场景渲染
4D Neural Voxel Splatting: Dynamic Scene Rendering with Voxelized Guassian Splatting
AI总结：
本文提出4D神经体素溅射方法，结合体素表示与神经高斯溅射，实现高效的动态场景建模，减少内存消耗并提升渲染质量。
摘要：
尽管3D高斯溅射(3D-GS)在新型视角合成中实现了高效渲染，但将其扩展到动态场景仍导致大量内存开销。为解决这一挑战，我们提出了4D神经体素溅射(4D-NVS)，结合体素表示与神经高斯溅射，通过学习变形场的紧凑神经体素集来建模时间动态。该设计大幅减少了内存消耗并加速了训练，同时保持高质量图像。我们进一步引入了新的视图细化阶段，通过定向优化改进具有挑战性的视角，维持全局效率的同时提升困难视角的渲染质量。实验表明，我们的方法在显著的内存减少和更快的训练速度下优于现有最先进方法，实现了具有优越视觉保真的实时渲染。
英文摘要：
Although 3D Gaussian Splatting (3D-GS) achieves efficient rendering for novel view synthesis, extending it to dynamic scenes still results in substantial memory overhead from replicating Gaussians across frames. To address this challenge, we propose 4D Neural Voxel Splatting (4D-NVS), which combines voxel-based representations with neural Gaussian splatting for efficient dynamic scene modeling. Instead of generating separate Gaussian sets per timestamp, our method employs a compact set of neural voxels with learned deformation fields to model temporal dynamics. The design greatly reduces memory consumption and accelerates training while preserving high image quality. We further introduce a novel view refinement stage that selectively improves challenging viewpoints through targeted optimization, maintaining global efficiency while enhancing rendering quality for difficult viewing angles. Experiments demonstrate that our method outperforms state-of-the-art approaches with significant memory reduction and faster training, enabling real-time rendering with superior visual fidelity.
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。
