Learning View-Dependent Splatting Kernels
We present a differentiable framework to automatically learn view-dependent 2D kernels in a splatting-based pipeline to improve reconstruction quality and representation efficiency for novel 3D view synthesis. Our volumetric primitive is defined as a bounding ellipsoid and a 3D-kernel latent vector. We first learn a projection network to output a 2D-kernel latent, taking the attributes of the ellipsoid and the 3D-kernel latent as input. Next, the result is sent to a decoder to produce a radially symmetric 2D kernel in terms of Mahalanobis distance, bounded by the projected ellipsoid. The neural networks along with per-primitive attributes are jointly optimized. The effectiveness of our ap...
| Authors | Huakeng Ding, Zhanpeng Liu, Fan Pei et al. |
|---|---|
| Primary category | cs.GR |
| Published | 25 May 2026 |
| Last updated at source | 25 May 2026 |
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