From the 1 of 9 linked papers with an AI index.
9 papers
NanoGS: Training-Free Gaussian Splat Simplification
Butian Xiong, Rong Liu, Tiantian Zhou +3
NanoGS is a training-free method that simplifies 3D Gaussian Splat models by merging nearby primitives using moment matching, reducing storage while keeping visual quality.
LTM: Large-scale Terrain Model for Wildfire-prone Landscapes
Xiao Fu, Yue Hu, Meida Chen +2
Accurate 3D terrain maps are essential for emergency response when assessing wildfire hazards. However, wildfire-prone regions often span vast areas where conventional reconstructi…
Universal Beta Splatting
Rong Liu, Zhongpai Gao, Benjamin Planche +8
We introduce Universal Beta Splatting (UBS), a unified framework that generalizes 3D Gaussian Splatting to N-dimensional anisotropic Beta kernels for explicit radiance field render…
Splat Feature Solver
Butian Xiong, Rong Liu, Kenneth Xu +2
Feature lifting has emerged as a crucial component in 3D scene understanding, enabling the attachment of rich image feature descriptors (e.g., DINO, CLIP) onto splat-based 3D repre…
IDU: Incremental Dynamic Update of Existing 3D Virtual Environments with New Imagery Data
Meida Chen, Luis Leal, Yue Hu +5
For simulation and training purposes, military organizations have made substantial investments in developing high-resolution 3D virtual environments through extensive imaging and 3…
Deformable Beta Splatting
Rong Liu, Dylan Sun, Meida Chen +2
3D Gaussian Splatting (3DGS) has advanced radiance field reconstruction by enabling real-time rendering. However, its reliance on Gaussian kernels for geometry and low-order Spheri…