GRTX: Efficient Ray Tracing for 3D Gaussian-Based Rendering
This program is tentative and subject to change.
3D Gaussian Splatting has gained widespread adoption across diverse applications due to its exceptional rendering performance and visual quality. While most existing methods rely on rasterization to render Gaussians, recent research has started investigating ray tracing approaches to overcome the fundamental limitations inherent in rasterization. However, current Gaussian ray tracing methods suffer from inefficiencies such as bloated acceleration structures and redundant node traversals, which greatly degrade ray tracing performance.
In this work, we present GRTX, a set of software and hardware optimizations that enable efficient ray tracing for 3D Gaussian-based rendering. First, we introduce a novel approach for constructing streamlined acceleration structures for Gaussian primitives. Our key insight is that anisotropic Gaussians can be treated as unit spheres through ray space transformations, which substantially reduces BVH size and traversal overhead. Second, we propose dedicated hardware support for traversal checkpointing within ray tracing units. This eliminates redundant node visits during multi-round tracing by resuming traversal from checkpointed nodes rather than restarting from the root node in each subsequent round. Our evaluation shows that GRTX significantly improves ray tracing performance compared to the baseline ray tracing method with negligible hardware cost.
This program is tentative and subject to change.
Mon 2 FebDisplayed time zone: Hobart change
15:50 - 17:10 | |||
15:50 20mTalk | GRTX: Efficient Ray Tracing for 3D Gaussian-Based Rendering Main Conference Junseo Lee Seoul National University, Sangyun Jeon Seoul National University, Jungi Lee Seoul National University, Junyong Park Seoul National University, Jaewoong Sim Seoul National University | ||
16:10 20mTalk | Splatonic: Architecture Support for 3D Gaussian Splatting SLAM via Sparse Processing Main Conference Xiaotong Huang Shanghai Jiao Tong University, He Zhu Shanghai Jiao Tong University, Tianrui Ma Institute of Computing Technology, Chinese Academy of Sciences, Yuxiang Xiong Shanghai Jiao Tong University, Fangxin Liu Shanghai Jiao Tong University, Zhezhi He Shanghai Jiao Tong University, Yiming Gan Institute of Computing Technology, Chinese Academy of Sciences, Zihan Liu Shanghai Jiao Tong University, Jingwen Leng Shanghai Jiao Tong University, Yu Feng Shanghai Jiao Tong University, Minyi Guo Shanghai Jiao Tong University | ||
16:30 20mTalk | FractalCloud: A Fractal-Inspired Architecture for Efficient Large-Scale Point Cloud Processing Main Conference Yuzhe Fu Duke University, Changchun Zhou Duke University, Hancheng Ye Duke University, Bowen Duan Duke University, Qiyu Huang Yale University, Chiyue Wei Duke University, Cong Guo Duke University, Hai "Helen" Li Duke University, Yiran Chen Duke University | ||
16:50 20mTalk | ORANGE: Exploring \underline{O}ckham's \underline{R}azor for Neural Rendering by \underline{A}ccelerating 3DGS on \underline{N}PUs with \underline{GE}MM-Friendly Blending and Balanced Workloads Main Conference Haomin Li Shanghai Jiao Tong University, Yue Liang Shanghai Jiao Tong University, Fangxin Liu Shanghai Jiao Tong University, Bowen Zhu Shanghai Jiao Tong University, Zongwu Wang Shanghai Jiao Tong University, Yu Feng Shanghai Jiao Tong University, Liqiang Lu Zhejiang University, Li Jiang Shanghai Jiaotong University, Haibing Guan Shanghai Jiao Tong University | ||