Gaussian Splatting SLAM
Imperial College London · Dyson (United Kingdom) · +1 more institution
Abstract
We present the first application of 3D Gaussian Splatting in monocular SLAM, the most fundamental but the hardest setup for Visual SLAM. Our method, which runs live at 3fps, utilises Gaussians as the only 3D representation, unifying the required representation for accurate, efficient tracking, mapping, and high-quality rendering. Designed for challenging monocular settings, our approach is seamlessly extendable to RGB-D SLAM when an external depth sensor is available. Several innovations are required to continuously reconstruct 3D scenes with high fidelity from a live camera. First, to move beyond the original 3DGS algorithm, which requires accurate poses from an offline Structure from Motion (SfM) system, we…
Citation impact
- FWCI
- 439.08
- Percentile
- 100%
- References
- 51
Authors
4Topics & keywords
- Computer science
- Gaussian
- Artificial intelligence
- Computer vision
- Computer graphics (images)