AdR-Gaussian:利用自适应半径加速高斯拼接

Xinzhe Wang, Ran Yi, Lizhuang Ma
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引用次数: 0

摘要

三维高斯拼接(3DGS)是近年来出现的一种显式三维表示方法,它实现了复杂场景的高质量重建和实时渲染。然而,光栅化流水线仍然存在不必要的开销,这些开销来自于可避免的串行高斯剔除,以及由于像素间需要渲染的高斯数量不同而导致的负载不均,这阻碍了3DGS的广泛推广和应用。为了加速高斯拼接,我们提出了 AdR-Gaussian,它将渲染阶段的部分串行剔除移到了更早的预处理阶段以实现并行剔除,采用自适应半径缩小每个高斯的渲染像素范围,并引入负载均衡方法以尽量减少像素并行渲染过程中的线程等待时间。我们的贡献体现在三个方面,使渲染速度提高了 310%,同时保持了与最先进技术相当甚至更好的质量。首先,我们建议在高斯并行预处理阶段,基于自适应半径对低溅射不透明度的高斯瓦片对进行早期剔除,从而通过高斯包围圈减少受影响瓦片的数量,从而减少不必要的开销,实现更快的渲染速度。其次,我们进一步提出了基于轴对齐包围盒的高斯拼接早期剔除方法,通过精确计算二维方向的高斯大小,更显著地减少了无效开销。第三,我们提出了一种像素线程负载平衡算法,压缩重负载像素的信息以减少线程等待时间,同时增强轻负载像素的信息以对冲渲染质量的损失。在三个数据集上的实验证明,我们的算法可以显著提高高斯拼接渲染的速度。
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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.
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