A 66.6 FPS High Quality Gaussian Splats Rendering FPGA Processor with Reconfigurable Computation Architecture
Recently, 3D Gaussian Splatting (3DGS) has been a novel solution in rendering 3D scene geometry. Unlike prior works, such as Neural Radiance Fields (NeRF), 3DGS can represent scenes using anisotropic covariance parameters as in Fig. 1 [1], enabling high quality rendering in both transparent and reflective scenes with reduced training data [2]. Key stages of rendering process is as follows:1) Frustum Culling - removing gaussians out of view bounds, 2) $2 D$ Feature Computation - computing image parameters from 3D scene parameters, 3) Sorting - aligning gaussians by depth order, 4) Volume Rendering (VR) - accumulating weighted sum of sorted Gaussians to obtain pixel colors.
Authors
- Wooyoung Jo (ORCID: https://orcid.org/0000-0003-3598-3572)
- Gwangtae Park (ORCID: https://orcid.org/0000-0002-9132-0311)
- Jongjun Park (ORCID: https://orcid.org/0009-0001-0168-1990)
- Won-Hoon Park
- Hongseok Lee (ORCID: https://orcid.org/0000-0003-0571-0522)
- Hoi-Jun Yoo
Institutions
- Korea Advanced Institute of Science and Technology (KR)
Publication Details
- Published
- 2024-11-18
- DOI
- https://doi.org/10.1109/a-sscc60305.2024.10848911
- Citations
- 5
- Primary Topic
- Advanced Vision and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.25