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

TL;DR

This paper introduces a real-time HDR radiance field reconstruction method using Gaussian splatting that incorporates depth of field and tonemapping, enabling immersive, refocusable cinematic rendering from multi-view LDR images.

Contribution

It presents a novel lightweight approach combining HDR reconstruction with depth of field effects using analytical Gaussian convolutions and tonemapping, improving upon existing radiance field methods.

Findings

Enables real-time HDR rendering with depth of field effects.

Outperforms state-of-the-art in cinematic quality and refocusing.

Supports multi-view LDR images with varying exposure and focus.

Abstract

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.