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

• URL: http://arxiv.org/abs/2406.07329v3
• Date: Fri, 23 Aug 2024 22:56:57 GMT
• Title: Cinematic Gaussians: Real-Time HDR Radiance Fields with Depth of Field
• Authors: Chao Wang, Krzysztof Wolski, Bernhard Kerbl, Ana Serrano, Mojtaba Bemana, Hans-Peter Seidel, Karol Myszkowski, Thomas Leimkühler,
• Abstract summary: Radiance field methods represent the state of the art in reconstructing complex scenes from multi-view photos. 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. We present a lightweight analytical based on 3D Gaussian Splatting that utilizes multi-view LDR images on varying exposure times, radiance of apertures, and focus distances as input to reconstruct a high-dynamic-range scene.
• Score: 23.92087253022495
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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