提升高斯射线渲染速度，实现实时高质量新视角合成。
RayGaussX: Accelerating Gaussian-Based Ray Marching for Real-Time and High-Quality Novel View Synthesis
- 引入空空间跳过与自适应采样，加速体积渲染过程。
- 训练提速5-12倍，渲染速度达50-80倍，PSNR提升最高0.56dB。
- 适合需要实时渲染的户外大场景应用，如AR/VR和自动驾驶。
RayGauss 在合成与室内场景的新视角合成中实现了顶尖渲染质量，通过不规则分布的椭圆基函数表示辐射场与密度场，并使用层次包围盒（BVH）进行体素射线投射渲染。然而其计算开销限制了真实世界场景的实时渲染。本文提出的 RayGaussX 改进了 RayGauss，引入空空间跳过、自适应采样、射线相干性增强及尺度正则化，有效减少误交情况。此外，提出新型密集化准则，改善远距离区域密度分布，显著提升大场景视觉质量。在真实世界数据集上，训练速度提升5至12倍，渲染速度达50至80倍（FPS），PSNR最高提升0.56 dB。项目主页含视频与代码：https://raygaussx.github.io/
原文摘要 · Abstract (English)
RayGauss has achieved state-of-the-art rendering quality for novel-view synthesis on synthetic and indoor scenes by representing radiance and density fields with irregularly distributed elliptical basis functions, rendered via volume ray casting using a Bounding Volume Hierarchy (BVH). However, its computational cost prevents real-time rendering on real-world scenes. Our approach, RayGaussX, builds on RayGauss by introducing key contributions that accelerate both training and inference. Specifically, we incorporate volumetric rendering acceleration strategies such as empty-space skipping and adaptive sampling, enhance ray coherence, and introduce scale regularization to reduce false-positive intersections. Additionally, we propose a new densification criterion that improves density distribution in distant regions, leading to enhanced graphical quality on larger scenes. As a result, RayGaussX achieves 5x to 12x faster training and 50x to 80x higher rendering speeds (FPS) on real-world datasets while improving visual quality by up to +0.56 dB in PSNR. Project page with videos and code: https://raygaussx.github.io/.
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