The repository contains submodules, thus please check it out with User camenduru was kind enough to provide a Colab template that uses this repo's source (status: August 2023!) for quick and easy access to the method. If the instructions below are too dry for you, go ahead and check it out here.

Jonathan Stephens made a fantastic step-by-step tutorial for setting up Gaussian Splatting on your machine, along with instructions for creating usable datasets from videos. Fridovich-Keil for helping with the comparisons. Tewari for proofreading earlier drafts also T. The authors thank the anonymous reviewers for their valuable feedback, P. The authors are grateful to Adobe for generous donations, the OPAL infrastructure from Université Côte d’Azur and for the HPC resources from GENCI–IDRIS (Grant 2022-AD011013409). This research was funded by the ERC Advanced grant FUNGRAPH No 788065. We demonstrate state-of-the-art visual quality and real-time rendering on several established = , First, starting from sparse points produced during camera calibration, we represent the scene with 3D Gaussians that preserve desirable properties of continuous volumetric radiance fields for scene optimization while avoiding unnecessary computation in empty space Second, we perform interleaved optimization/density control of the 3D Gaussians, notably optimizing anisotropic covariance to achieve an accurate representation of the scene Third, we develop a fast visibility-aware rendering algorithm that supports anisotropic splatting and both accelerates training and allows realtime rendering.

We introduce three key elements that allow us to achieve state-of-the-art visual quality while maintaining competitive training times and importantly allow high-quality real-time (≥ 30 fps) novel-view synthesis at 1080p resolution. For unbounded and complete scenes (rather than isolated objects) and 1080p resolution rendering, no current method can achieve real-time display rates.

However, achieving high visual quality still requires neural networks that are costly to train and render, while recent faster methods inevitably trade off speed for quality. We further provide the reference images used to create the error metrics reported in the paper, as well as recently created, pre-trained models.Ībstract: Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. This repository contains the official authors implementation associated with the paper '3D Gaussian Splatting for Real-Time Radiance Field Rendering', which can be found here. | T&T+DB COLMAP (650MB) | Pre-trained Models (14 GB) | Viewers for Windows (60MB) | Evaluation Images (7 GB) | | Webpage | Full Paper | Video | Other GRAPHDECO Publications | FUNGRAPH project page | 3D Gaussian Splatting for Real-Time Radiance Field Renderingīernhard Kerbl*, Georgios Kopanas*, Thomas Leimkühler, George Drettakis (* indicates equal contribution)

Alternative to vray for blender