HDR-GS: Efficient High Dynamic Range Novel View Synthesis at 1000x Speed
  via Gaussian Splatting
Authors: name: Yuanhao Cai, name: Zihao Xiao, name: Yixun Liang, name: Yulun Zhang, name: Xiaokang Yang, name: Yaoyao Liu, name: Alan Yuille

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.