Radiance field methods represent the state of the art in reconstructing
complex scenes from multi-view photos. However, these reconstructions often
suffer from one or both of the following limitations: First, they typically
represent scenes in low dynamic range (LDR), which restricts their use to
evenly lit environments and hinders immersive viewing experiences. Secondly,
their reliance on a pinhole camera model, assuming all scene elements are in
focus in the input images, presents practical challenges and complicates
refocusing during novel-view synthesis. Addressing these limitations, we
present a lightweight method based on 3D Gaussian Splatting that utilizes
multi-view LDR images of a scene with varying exposure times, apertures, and
focus distances as input to reconstruct a high-dynamic-range (HDR) radiance
field. By incorporating analytical convolutions of Gaussians based on a
thin-lens camera model as well as a tonemapping module, our reconstructions
enable the rendering of HDR content with flexible refocusing capabilities. We
demonstrate that our combined treatment of HDR and depth of field facilitates
real-time cinematic rendering, outperforming the state of the art.

以基于 3D 高斯喷墨技术的轻量级方法为基础，利用多视图低动态范围（LDR）图像，通过变化的曝光时间、光圈和聚焦距离重建高动态范围（HDR）光辐射场，实现灵活的重焦渲染，同时结合 HDR 和景深的处理，能够在实时电影渲染中表现出优异的性能。

电影高斯：具有景深的实时 HDR 辐射场

Cinematic Gaussians: Real-Time HDR Radiance Fields with Depth of Field

Radiance Field methods have recently revolutionized novel-view synthesis of
scenes captured with multiple photos or videos. However, achieving high visual
quality still requires neural networks that are costly to train and render,
while recent faster methods inevitably trade off speed for quality. For
unbounded and complete scenes (rather than isolated objects) and 1080p
resolution rendering, no current method can achieve real-time display rates. We
introduce three key elements that allow us to achieve state-of-the-art visual
quality while maintaining competitive training times and importantly allow
high-quality real-time (>= 30 fps) novel-view synthesis at 1080p resolution.
First, starting from sparse points produced during camera calibration, we
represent the scene with 3D Gaussians that preserve desirable properties of
continuous volumetric radiance fields for scene optimization while avoiding
unnecessary computation in empty space; Second, we perform interleaved
optimization/density control of the 3D Gaussians, notably optimizing
anisotropic covariance to achieve an accurate representation of the scene;
Third, we develop a fast visibility-aware rendering algorithm that supports
anisotropic splatting and both accelerates training and allows realtime
rendering. We demonstrate state-of-the-art visual quality and real-time
rendering on several established datasets.

我们提出了三个关键要素，可实现高质量的实时（大于等于 30 帧 / 秒）1080p 分辨率的新视图合成，其中包括使用 3D 高斯函数表示场景、优化 3D 高斯函数的相关参数以准确表示场景，并开发了一种快速的可见性感知渲染算法，以加速训练并实现实时渲染。