This paper is an overview to Visual Simultaneous Localization and Map** (V-SLAM). We
discuss the basic definitions in the SLAM and vision system fields and provide a review of …

A point cloud is a set of points defined in a 3D metric space. Point clouds have become one
of the most significant data formats for 3D representation and are gaining increased …

Reconstructing accurate 3D scenes from images is a long-standing vision task. Due to the ill-
posedness of the single-image reconstruction problem, most well-established methods are …

Despite recent success in incorporating learning into point cloud registration, many works
focus on learning feature descriptors and continue to rely on nearest-neighbor feature …

This paper addresses the challenge of reconstructing 3D indoor scenes from multi-view
images. Many previous works have shown impressive reconstruction results on textured …

We introduce PREDATOR, a model for pairwise pointcloud registration with deep attention
to the overlap region. Different from previous work, our model is specifically designed to …

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

Abstract 6D pose estimation from a single RGB image is a fundamental task in computer
vision. The current top-performing deep learning-based methods rely on an indirect strategy …

We present iNeRF, a framework that performs mesh-free pose estimation by" inverting" a
Neural Radiance Field (NeRF). NeRFs have been shown to be remarkably effective for the …

We study the problem of extracting correspondences between a pair of point clouds for
registration. For correspondence retrieval, existing works benefit from matching sparse …