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

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

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

In recent years, unmanned aerial vehicles (UAVs) have been confirmed as a powerful tool
for countless applications in nearly every industry, in which collision avoidance plays a vital …

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

We propose a dense neural simultaneous localization and map** (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 …

We propose CLIP-Fields, an implicit scene model that can be used for a variety of tasks,
such as segmentation, instance identification, semantic search over space, and view …

We present Loc-NeRF, a real-time vision-based robot localization approach that combines
Monte Carlo localization and Neural Radiance Fields (NeRF). Our system uses a pre-trained …

Recent implicit neural representations have shown great results for novel view synthesis.
However, existing methods require expensive per-scene optimization from many views …