Mip-Splatting在低分辨率下优于3DGS和3DGS+EWA。 本文提出了Mip-Splatting,这是对3D高斯Splatting的修改,引入了两个新颖的滤波器,即3D平滑滤波器和2D Mip滤波器,以在任意尺度实现无混叠的渲染。我们的3D平滑滤波器有效地限制了高斯原语的最大频率,以匹配训练图像施加的采样约束,而2D Mip滤波器则近似盒滤波器以...
Mip-Splatting Mip-Splatting: Alias-free3D Gaussian Splatting 单位:图宾根大学, 图宾根AI中心, 上科大等 主页:https://niujinshuchong.github.io/mip-splatting/ 代码:https://github.com/autonomousvision/mip-splatting 论文:https://arxiv.org/abs/2311.16493 CVPR 2024论文和开源项目合集请戳—>https://...
Mip-Splatting: Alias-free 3D Gaussian Splatting Zehao Yu·Anpei Chen·Binbin Huang·Torsten Sattler·Andreas Geiger CVPR 2024 Best Student Paper Paper|arXiv|Project Page|Online Viewer We introduce a 3D smoothing filter and a 2D Mip filter for 3D Gaussian Splatting (3DGS), eliminating multiple...
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Recently, 3D Gaussian Splatting has demonstrated impressive novel view synthesis results, reaching high fidelity and efficiency. However, strong artifacts can be observed when changing the sampling rate, \eg, by changing focal length or camera distance. We find that the source for this phenomenon ca...
项目主页: https://niujinshuchong.github.io/mip-splatting/ 最近,3D Gaussian泼溅展示的新颖视图合成结果,实现了高保真度和高效率,令人印象深刻。然而,当采样率改变时,可以观察到强烈的伪影,例如,通过改变焦距或相机距离。我们发现这种现象的来源可以归结为缺乏3D频率限制和使用2D伸缩滤波器。为了解决这个问题,我们引...
The structure of the resulting multiresolution rendering algorithm is very similar to wavelet splatting. Several existing classes of pyramids are discussed, and their limitations indicated. To enhance the approximation quality of visualizations from reduced data (higher levels of the pyramid), two ...
北京时间 6 月 20 日凌晨,CVPR 2024 正式公布了最佳论文、最佳学生论文等奖项。其中,获得最佳论文的有两篇文章 ——BioCLIP 和 Mip-Splatting。 据Mip-Splatting 论文一作 Zehao Yu 的导师、图宾根大学教授 Andreas Geiger 透露,这篇论文从想法公布到成稿,只用了一个月的时间。
Splatting Introduction VolumeRenderingTechniques • Hybridtechnique • Combinationofbothapproaches • Shear-warp(LacrouteandLevoy,Proc.SIGGRAPH94,451,1994) Introduction VolumeRenderingTechniques • Image-ordertechnique • Involvestwosteps •
3D Gaussian Splatting (3DGS) has attracted great attention in novel view synthesis because of its superior rendering efficiency and high fidelity. However, the trained Gaussians suffer from severe zooming degradation due to non-adjustable representation derived from single-scale training. Though some ...