Gaussian-SLAM: Photo-realistic Dense SLAM with Gaussian Splatting

Specifically, earlier works focus a scene representation. The new representation enables on tracking using various scene representations like interactive-time reconstruction and photo-realistic rendering feature point clouds [15, 26, 40], surfels [53, 71], depth of real-world and synthetic scenes. We propose novel maps [43, 58], or implicit representations [14, 42, 44]. Later strategies for seeding and optimizing Gaussian splats to works focused more on the map quality and density. With extend their use from multiview offline scenarios to sequential the advent of powerful neural scene representations like monocular RGBD input data setups. In addition, we neural radiance fields [38] that allow for high fidelity viewsynthesis, extend Gaussian splats to encode geometry and experiment a rapidly growing body of dense neural SLAM with tracking against this scene representation. Our methods [19, 34, 51, 60, 62, 64, 81, 84] has been developed.