RAVE: 适应性速率的3D高斯点云编码
RAVE: Rate-Adaptive Visual Encoding for 3D Gaussian Splatting
AI总结：
RAVE提出了一种灵活的3D高斯点云压缩方法，支持动态速率控制，实现高效高质量的压缩，适用于沉浸式应用。
摘要：
近年来，神经场景表示的进步已将沉浸式多媒体推向新高度，3D高斯点云（3DGS）使实时逼真渲染成为可能。尽管其效率高，但3DGS仍然面临内存需求大和训练成本高的问题，推动了压缩技术的发展。现有方法却以固定速率运行，限制了对变化带宽和设备约束的适应性。在本文中，我们提出了一种灵活的3DGS压缩方案，支持在预定义范围内任意速率的插值。我们的方法计算轻量，无需重新训练即可适应任何速率，并在广泛的运行点上保持渲染质量。实验表明，该方法实现了高效、高质量的压缩，同时提供动态速率控制，使其适用于沉浸式应用的实践部署。代码可在https://github.com/inspiros/RAVE上获得。
英文摘要：
Recent advances in neural scene representations have transformed immersive multimedia, with 3D Gaussian Splatting (3DGS) enabling real-time photorealistic rendering. Despite its efficiency, 3DGS suffers from large memory requirements and costly training procedures, motivating efforts toward compression. Existing approaches, however, operate at fixed rates, limiting adaptability to varying bandwidth and device constraints. In this work, we propose a flexible compression scheme for 3DGS that supports interpolation at any rate between predefined bounds. Our method is computationally lightweight, requires no retraining for any rate, and preserves rendering quality across a broad range of operating points. Experiments demonstrate that the approach achieves efficient, high-quality compression while offering dynamic rate control, making it suitable for practical deployment in immersive applications. The code is available at https://github.com/inspiros/RAVE.
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。
