The fusion of low-cost unmanned aerial systems (UAS) with advanced photogrammetric techniques has revolutionized 3D terrain reconstruction, enabling the automated creation of detailed models. Concurrently, the advent of 3D Gaussian Splatting has introduc...
This work introduces FlashGS, an open-source CUDA Python library, designed to facilitate the efficient differentiable rasterization of 3D Gaussian Splatting through algorithmic and kernel-level optimizations. FlashGS is developed based on the observations from a comprehensive analysis of the rendering proc...
DOGS: Distributed-Oriented Gaussian Splatting for Large-Scale 3D Reconstruction Via Gaussian Consensus [Project Page | arXiv] (NeurIPS 2024) 1. Introduction Our method accelerates the training of 3DGS by 6+ times when evaluated on large-scale scenes while concurrently achieving state-of-the-art ...
按图像的经验,超分这种低级视觉任务用个 U-net 也是够的。 质量高的一个重要原因是直接用 Gaussian Splatting 而没有用 Tri-plane 之类的低维表示。不过话说回来,Tri-plane 作为了一种三阶张量(也就是体素)的近似,原理上是支持扩 channel,或者多选几组基(比如用不同于 x-y-z 的坐标系)来提升参数量从而改...
We first run the provided script to pre-process a large-scale scene into several blocks: cdscripts/preprocess ./preprocess_large_scale_data.sh 0 urban3d gaussian_splatting Visualize scene splitting Please check and compilemy modification of COLMAP. After installation, launch COLMAP's GUI. I exten...
Gaussian Splattingis a new technique to capture and display volumetric data usingNeural Radiance Fields, point clouds and billboards. In more simple terms, it’s an advanced way of allowing people to capture and display the real world with a level of unmatched visual fid...
The advancement of real-time 3D scene reconstruction and novel view synthesis has been significantly propelled by 3D Gaussian Splatting (3DGS). However, effectively training large-scale 3DGS and rendering it in real-time across various scales remains challenging. This paper introduces CityGaussian (...
In contrast to previous LRMs that can only reconstruct objects, by predicting per-pixel Gaussians, GS-LRM naturally handles scenes with large variations in scale and complexity. We show that our model can work on both object and scene captures by training it on Objaverse and RealEstate10K ...
We propose GS-LRM, a scalable large reconstruction model that can predict high-quality 3D Gaussian primitives from 2-4 posed sparse images in 0.23 seconds on single A100 GPU. Our model features a very simple transformer-based architecture; we patchify input posed images, pass the concatenated mu...
Geometry-Aware 3D Gaussian Representation for Real-Time Rendering of Large-Scale Scenes - SCUT-BIP-Lab/Geo_gs