In just over a year, 3D Gaussian Splatting (3DGS) has made waves in computer vision for its remarkable speed, simplicity, and visual quality. Yet, even scenes of a single room can exceed a gigabyte in size, making it difficult to scale up to larger environments, like city blocks. In this talk, we’ll explore compression techniques to reduce the 3DGS memory footprint. We’ll dive deeply into our novel approach, Self-Organizing Gaussians, which proposes to map splatting attributes into a 2D grid, using a high-performance parallel linear assignment sorting developed to reorganize the splats on the fly. This grid assignment allows us to leverage traditional 2D image compression techniques like JPEG to efficiently store 3D data. Our method is quick and easy to decompress and provides a surprisingly competitive compression ratio. The drastically reduced memory requirements make this method perfect for efficiently streaming 3D scenes at large scales, which is especially useful for AR, VR and gaming applications.
ECCV 2024 Paper
is a Research Associate at the Computer Vision & Graphics group at Fraunhofer HHI and is pursuing a PhD at Humboldt University Berlin. His research focuses on representing 3D scenes and virtual humans.
SOCIAL SHARE CARD GENERATOR