AdR-Gaussian: Accelerating Gaussian Splatting with Adaptive Radius
3D Gaussian Splatting (3DGS) is a recent explicit 3D representation that has achieved high-quality reconstruction and real-time rendering of complex scenes. However, the rasterization pipeline still suffers from unnecessary overhead resulting from avoidable serial Gaussian culling, and uneven load due to the distinct number of Gaussian to be rendered across pixels, which hinders wider promotion and application of 3DGS. In order to accelerate Gaussian splatting, we propose AdR-Gaussian, which moves part of serial culling in Render stage into the earlier Preprocess stage to enable parallel culling, employing adaptive radius to narrow the rendering pixel range for each Gaussian, and introduces a load balancing method to minimize thread waiting time during the pixel-parallel rendering. Our contributions are threefold, achieving a rendering speed of 310% while maintaining equivalent or even better quality than the state-of-the-art. Firstly, we propose to early cull Gaussian-Tile pairs of low splatting opacity based on an adaptive radius in the Gaussian-parallel Preprocess stage, which reduces the number of affected tile through the Gaussian bounding circle, thus reducing unnecessary overhead and achieving faster rendering speed. Secondly, we further propose early culling based on axis-aligned bounding box for Gaussian splatting, which achieves a more significant reduction in ineffective expenses by accurately calculating the Gaussian size in the 2D directions. Thirdly, we propose a balancing algorithm for pixel thread load, which compresses the information of heavy-load pixels to reduce thread waiting time, and enhance information of light-load pixels to hedge against rendering quality loss. Experiments on three datasets demonstrate that our algorithm can significantly improve the Gaussian Splatting rendering speed.
Code (0)
등록된 구현이 없습니다.
Tasks
3DGSMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
SA-GS: Scale-Adaptive Gaussian Splatting for Training-Free Anti-Aliasing
In this paper, we present a Scale-adaptive method for Anti-aliasing Gaussian Splatting (SA-GS). While the state-of-the-art method Mip-Splatting needs modifying the training procedure of Gaussian splatting, our method fun…
ViewSplat: View-Adaptive 3D Gaussian Splatting for Feed-Forward Synthesis
We present ViewSplat, a view-adaptive 3D Gaussian splatting network for novel view synthesis from unposed images. While recent feed-forward 3D Gaussian splatting has significantly accelerated 3D scene reconstruction by b…
Novel View SynthesisSG-Splatting: Accelerating 3D Gaussian Splatting with Spherical Gaussians
3D Gaussian Splatting is emerging as a state-of-the-art technique in novel view synthesis, recognized for its impressive balance between visual quality, speed, and rendering efficiency. However, reliance on third-degree …
Computational EfficiencyNovel View SynthesisFi-Gaussian: Frequency-Aware Implicit Gaussian Splatting for Single Image Dehazing
Single image dehazing continues to be hindered by the loss of high-frequency details and the difficulty of accurate physical scattering modeling. To address these issues, we propose Fi-Gaussian, a frequency-aware implici…
Single Image DehazingPoint CloudsCountingFruit: Real-Time 3D Fruit Counting with Language-Guided Semantic Gaussian Splatting
Accurate fruit counting in real-world agricultural environments is a longstanding challenge due to visual occlusions, semantic ambiguity, and the high computational demands of 3D reconstruction. Existing methods based on…
3D ReconstructionNeural Rendering