Dense simultaneous localization and mapping (SLAM) is crucial for robotics and augmented
reality applications. However current methods are often hampered by the non-volumetric or …

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 …

Neural implicit representations have recently demonstrated compelling results on dense
Simultaneous Localization And Mapping (SLAM) but suffer from the accumulation of errors …

Neural implicit representations have recently become popular in simultaneous localization
and mapping (SLAM), especially in dense visual SLAM. However, existing works either rely …

We propose a dense neural simultaneous localization and mapping (SLAM) approach for
monocular RGBD input which anchors the features of a neural scene representation in a …

Abstract Neural Radiance Field (NeRF) has emerged as a powerful paradigm for scene
representation, offering high-fidelity renderings and reconstructions from a set of sparse and …

Neural RGBD SLAM techniques have shown promise in dense Simultaneous Localization
And Mapping (SLAM) yet face challenges such as error accumulation during camera …

Abstract Recent work in Visual Odometry and SLAM has shown the effectiveness of using
deep network backbones. Despite excellent accuracy, such approaches are often expensive …

We present a dense simultaneous localization and mapping (SLAM) method that uses 3D
Gaussians as a scene representation. Our approach enables interactive-time reconstruction …

In this letter, we present a neural field-based real-time monocular mapping framework for
accurate and dense Simultaneous Localization and Mapping (SLAM). Recent neural …