Ewa Miazga

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Ewa Miazga

Ewa Miazga

@miazga02

Lugano, Switzerland Katılım Eylül 2025
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Ewa Miazga
Ewa Miazga@miazga02·
We just released the code! It can be found at github.com/ewaMiazga/NASG… Upon successfully completing my project under the supervision of @Arcanous98, I am looking for a Master’s internship in Zurich. If you are interested in collaborating, contact me!
Jorge Condor@Arcanous98

Together with @miazga02, we are announcing today Beyond Spherical Harmonics. We explored if we could do better than Spherical Harmonics in #3DGS, and found a few very interesting takeways in the process. Code in #gsplat and paper released today arcanous98.github.io/projectPages/b… 🧵🧵🧵

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MrNeRF
MrNeRF@janusch_patas·
Beyond Spherical Harmonics: Rethinking Appearance Models for Radiance Reconstruction Abstract: View-dependent appearance modeling remains a challenging problem in novel-view synthesis and reconstruction. Accurately representing complex angular effects often requires substantial memory and computational resources. For new learning-based methods, a common approach is to rely on SH. However, capturing high-frequency phenomena such as specular reflections demands high-order expansions, which increase memory usage and computational cost. Consequently, most methods employ low-order SH, which limits the ability to model complex view-dependent effects, resulting in overly smooth or diffuse representations. To address these limitations, we systematically evaluate a wide range of spherical functions in the context of scene reconstruction. Some of them are introduced to graphics and computer vision for the first time in this paper. Based on the insights from the experiment, we develop a novel spherical formulation, the Normalized Anisotropic Spherical Gabor function that enables efficient modeling and learning of high-frequency appearance effects while maintaining compact representation. Compared to existing approaches, our function achieves higher-quality reconstruction of view-dependent phenomena such as glints, while being up to five times more memory-efficient and more efficient to evaluate. We validate its performance in radiance-field reconstruction tasks.
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