Recently, Gaussian splatting has emerged as a robust technique for representing 3D scenes, enabling real-time rasterization and high-fidelity rendering. However, Gaussians' inherent radial symmetry and smoothness constraints limit their ability to represent complex shapes, often requiring thousands of primitives to approximate detailed geometry. We introduce Deformable Radial Kernel (DRK), which extends Gaussian splatting into a more general and flexible framework. Through learnable radial bases with adjustable angles and scales, DRK efficiently models diverse shape primitives while enabling precise control over edge sharpness and boundary curvature. iven DRK's planar nature, we further develop accurate ray-primitive intersection computation for depth sorting and introduce efficient kernel culling strategies for improved rasterization efficiency. Extensive experiments demonstrate that DRK outperforms existing methods in both representation efficiency and rendering quality, achieving state-of-the-art performance while dramatically reducing primitive count.
Our DRK representation extends Gaussian kernels with learnable radial bases, enabling the modeling of diverse shape primitives. Additionally, parameters are introduced to control the sharpness and boundary curvature of these primitives. The following video showcase the effectiveness of each parameter.
We demonstrate the flexibility of DRK in fitting complex shapes using diverse primitives. The following video highlights DRK's fitting results on various shapes.
Thanks to the flexibility of DRK, triangle/polygon meshes can be converted into DRK representations without requiring training views, rendering, or model optimization. This enables the seamless conversion of millions of 3D assets into DRK representations for real-time rendering. The following video showcases the conversion results.
Train-free conversion from meshes to DRK enables millions of 3D assets to seamlessly integrate into high-fidelity reconstructed real scenes. Both types of assets are rendered together within a unified rendering pipeline. The following video showcases the resulting composed renderings.
@article{huang2024deformable,
title={Deformable Radial Kernel Splatting},
author={Huang, Yi-Hua and Lin, Ming-Xian and Sun, Yang-Tian and Yang, Ziyi and Lyu, Xiaoyang and Cao, Yan-Pei and Qi, Xiaojuan},
journal={arXiv preprint arXiv:2412.11752},
year={2024}
}There are some excellent works that highly related to our work. We list some of them here.
We sincerely thank BinBin Huang for his valuable suggestions and feedbacks. He is an excellent researcher in the fields of 3D representation and reconstruction.
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