Neural implicit representations have recently shown encouraging results in various
domains, including promising progress in simultaneous localization and mapping (SLAM) …

MonoScene proposes a 3D Semantic Scene Completion (SSC) framework, where the dense
geometry and semantics of a scene are inferred from a single monocular RGB image …

We present ESLAM, an efficient implicit neural representation method for Simultaneous
Localization and Mapping (SLAM). ESLAM reads RGB-D frames with unknown camera …

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 …

Traditionally, 3D indoor scene reconstruction from posed images happens in two phases:
per-image depth estimation, followed by depth merging and surface reconstruction …

Neural implicit scene representations have recently shown encouraging results in dense
visual SLAM. However existing methods produce low-quality scene reconstruction and low …

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

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

We propose an algorithm, 4DRegSDF, for the spacetime surface regularization to improve
the fidelity of neural rendering and reconstruction in dynamic scenes. The key idea is to …

A key challenge in neural 3D scene reconstruction from monocular images is to fuse
features back projected from various views without any depth or occlusion information. We …