A Hierarchical 3D Gaussian Representation for Real-Time Rendering of Very Large Datasets
By Bernhard Kerbl, Andréas Meuleman, Georgios Kopanas, Michael Wimmer, A. Lanvin, George Drettakis. ACM Transactions on Graphics, 2024
347 citations recorded by the source corpus.
Novel view synthesis has seen major advances in recent years, with 3D Gaussian splatting offering an excellent level of visual quality, fast training and real-time rendering. However, the resources needed for training and rendering inevitably limit the size of the captured scenes that can be represented with good visual quality. We introduce a hierarchy of 3D Gaussians that preserves visual quality for very large scenes, while offering an efficient Level-of-Detail (LOD) solution for efficient rendering of distant content with effective level selection and smooth transitions between levels. We introduce a divide-and-conquer approach that allows us to train very large scenes in independent chunks. We consolidate the chunks into a hierarchy that can be optimized to further improve visual quality of Gaussians merged into intermediate nodes. Very large captures typically have sparse coverage of the scene, presenting many challenges to the original 3D Gaussian splatting training method; we adapt and regularize training to account for these issues. We present a complete solution, that enables real-time rendering of very large scenes and can adapt to available resources thanks to our LOD method. We show results for captured scenes with up to tens of thousands of images with a simple and affordable rig, covering trajectories of up to several kilometers and lasting up to one hour.
Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/da40e92fc04ef8bd5895b4ea8e829d85273c08b0
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Citations in the corpus
- Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D Gaussians (2024, methodology): “As indicated by the highlighted arrows, Octree-GS consistently demonstrates superior visual quality compared to state-of-the-art methods Hierarchical-GS [2] and Scaffold-GS [3].”