title: HDR-GS: Efficient High Dynamic Range Novel View Synthesis at 1000x Speed
    via Gaussian Splatting
summary: High dynamic range (HDR) novel view synthesis (NVS) aims to create
  photorealistic images from novel viewpoints using HDR imaging techniques. The
  rendered HDR images capture a wider range of brightness levels containing more
  details of the scene than normal low dynamic range (LDR) images. Existing HDR
  NVS methods are mainly based on NeRF. They suffer from long training time and
  slow inference speed. In this paper, we propose a new framework, High Dynamic
  Range Gaussian Splatting (HDR-GS), which can efficiently render novel HDR views
  and reconstruct LDR images with a user input exposure time. Specifically, we
  design a Dual Dynamic Range (DDR) Gaussian point cloud model that uses
  spherical harmonics to fit HDR color and employs an MLP-based tone-mapper to
  render LDR color. The HDR and LDR colors are then fed into two Parallel
  Differentiable Rasterization (PDR) processes to reconstruct HDR and LDR views.
  To establish the data foundation for the research of 3D Gaussian
  splatting-based methods in HDR NVS, we recalibrate the camera parameters and
  compute the initial positions for Gaussian point clouds. Experiments
  demonstrate that our HDR-GS surpasses the state-of-the-art NeRF-based method by
  3.84 and 1.91 dB on LDR and HDR NVS while enjoying 1000x inference speed and
  only requiring 6.3% training time.
paper_authors._id: 6653f1fa67b5ae524c6be57f
paper_authors.name: Yuanhao Cai
paper_authors._id: 6653f1fa67b5ae524c6be580
paper_authors.name: Zihao Xiao
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paper_authors.fullname: Zihao Xiao
paper_authors.type: user
paper_authors.user: zxiao10
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paper_authors.name: Yixun Liang
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paper_authors.user: lyxun
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paper_authors.name: Yulun Zhang
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paper_authors.fullname: Yulun Zhang
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paper_authors.user: yulunzhang
paper_authors._id: 6653f1fa67b5ae524c6be583
paper_authors.name: Xiaokang Yang
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paper_authors.fullname: yangxiaokang
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paper_authors.user: zicheqingluo
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paper_authors.name: Yaoyao Liu
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paper_authors.fullname: Yaoyao Liu
paper_authors.type: user
paper_authors.user: yaoyaoliu
paper_authors._id: 6653f1fa67b5ae524c6be585
paper_authors.name: Alan Yuille
paper_title: HDR-GS: Efficient High Dynamic Range Novel View Synthesis at 1000x Speed
    via Gaussian Splatting
paper_summary: High dynamic range (HDR) novel view synthesis (NVS) aims to create
  photorealistic images from novel viewpoints using HDR imaging techniques. The
  rendered HDR images capture a wider range of brightness levels containing more
  details of the scene than normal low dynamic range (LDR) images. Existing HDR
  NVS methods are mainly based on NeRF. They suffer from long training time and
  slow inference speed. In this paper, we propose a new framework, High Dynamic
  Range Gaussian Splatting (HDR-GS), which can efficiently render novel HDR views
  and reconstruct LDR images with a user input exposure time. Specifically, we
  design a Dual Dynamic Range (DDR) Gaussian point cloud model that uses
  spherical harmonics to fit HDR color and employs an MLP-based tone-mapper to
  render LDR color. The HDR and LDR colors are then fed into two Parallel
  Differentiable Rasterization (PDR) processes to reconstruct HDR and LDR views.
  To establish the data foundation for the research of 3D Gaussian
  splatting-based methods in HDR NVS, we recalibrate the camera parameters and
  compute the initial positions for Gaussian point clouds. Experiments
  demonstrate that our HDR-GS surpasses the state-of-the-art NeRF-based method by
  3.84 and 1.91 dB on LDR and HDR NVS while enjoying 1000x inference speed and
  only requiring 6.3% training time.
paper_ai_summary: A novel HDR Gaussian Splatting (HDR-GS) framework efficiently synthesizes HDR and LDR images from novel viewpoints, outperforming NeRF in quality and speed.
paper_ai_keywords: NeRF HDR NVS LDR images Dual Dynamic Range (DDR) Gaussian point cloud model spherical harmonics MLP-based tone-mapper Parallel Differentiable Rasterization (PDR) Gaussian point clouds