Nobuyuki Umetani — accepted AI-conference papers

2026

DiffSoup: Direct Differentiable Rasterization of Triangle Soup for Extreme Radiance Field Simplification

Kenji Tojo, Bernd Bickel, Nobuyuki Umetani

CVPR 2026

Radiance field reconstruction aims to recover high-quality 3D representations from multi-view RGB images. Recent advances, such as 3D Gaussian splatting, enable real-time rendering with high visual fidelity on sufficiently powerful graphics hardware. However, efficient online transmission and render

Cited by 0 Source ↗code ↗Scholar ↗

2026

Neural Gabor Splatting: Enhanced Gaussian Splatting with Neural Gabor for High-frequency Surface Reconstruction

Haato Watanabe, Nobuyuki Umetani

CVPR 2026

Recent years have witnessed the rapid emergence of 3D Gaussian Splatting (3DGS) as a powerful approach for 3D reconstruction and novel view synthesis. Its explicit representation with Gaussian primitives enables fast training, real-time rendering, and convenient post-processing such as editing and s

Cited by 0 Source ↗Scholar ↗