1 citations · 2 across the 6 of their papers we have counts for
6 papers
TC-GS: A Faster Gaussian Splatting Module Utilizing Tensor Cores
Zimu Liao, Jifeng Ding, Siwei Cui +7
3D Gaussian Splatting (3DGS) renders pixels by rasterizing Gaussian primitives, where conditional alpha-blending dominates the computational cost in the rendering pipeline. This pa…
GS-Cache: A GS-Cache Inference Framework for Large-scale Gaussian Splatting Models
Miao Tao, Yuanzhen Zhou, Haoran Xu +10
Rendering large-scale 3D Gaussian Splatting (3DGS) model faces significant challenges in achieving real-time, high-fidelity performance on consumer-grade devices. Fully realizing t…
Towards Efficient Pre-training: Exploring FP4 Precision in Large Language Models
Jiecheng Zhou, Ding Tang, Rong Fu +8
The burgeoning computational demands for training large language models (LLMs) necessitate efficient methods, including quantized training, which leverages low-bit arithmetic opera…
Fisheye-GS: Lightweight and Extensible Gaussian Splatting Module for Fisheye Cameras
Zimu Liao, Siyan Chen, Rong Fu +9
Recently, 3D Gaussian Splatting (3DGS) has garnered attention for its high fidelity and real-time rendering. However, adapting 3DGS to different camera models, particularly fisheye…
PackMamba: Efficient Processing of Variable-Length Sequences in Mamba training
Haoran Xu, Ziqian Liu, Rong Fu +5
With the evolution of large language models, traditional Transformer models become computationally demanding for lengthy sequences due to the quadratic growth in computation with r…
FlashGS: Efficient 3D Gaussian Splatting for Large-scale and High-resolution Rendering
Guofeng Feng, Siyan Chen, Rong Fu +7
This work introduces FlashGS, an open-source CUDA Python library, designed to facilitate the efficient differentiable rasterization of 3D Gaussian Splatting through algorithmic and…