Scalability of 3D Gaussian Splatting vs NeRF-based SLAM on Large-Scale Indoor Benchmarks

Recently, map representations based on radiance fields such as 3D Gaussian Splatting and NeRF, which excellent for realistic depiction, have attracted considerable attention, leading to attempts to combine them with SLAM. While these approaches can build highly realistic maps, large-scale SLAM still remains a challenge because they require a large number of Gaussian images for mapping and adjacent images as keyframes for tracking. We propose a novel 3D Gaussian Splatting SLAM method, VIGS SLAM, that utilizes sensor fusion of RGB-D and IMU sensors for large-scale indoor environments. To reduce

Research goal: How does the scalability of 3D Gaussian Splatting for SLAM compare to NeRF-based approaches when evaluated on large-scale indoor benchmarks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.