Augmented reality (AR) has proven to be an invaluable interactive medium to reduce
cognitive load by bridging the gap between the task-at-hand and relevant information by …

SLAM is an abbreviation for simultaneous localization and mapping, which is a technique for
estimating sensor motion and reconstructing structure in an unknown environment …

Self-supervised monocular depth estimation networks are trained to predict scene depth
using nearby frames as a supervision signal during training. However, for many …

In this work, we present a dense tracking and mapping system named Vox-Fusion, which
seamlessly fuses neural implicit representations with traditional volumetric fusion methods …

Efficiently reconstructing complex and intricate surfaces at scale is a long-standing goal in
machine perception. To address this problem we introduce Deep Local Shapes (DeepLS), a …

Low textured scenes are well known to be one of the main Achilles heels of geometric
computer vision algorithms relying on point correspondences, and in particular for visual …

We present an approach to indoor scene reconstruction from RGB-D video. The key idea is
to combine geometric registration of scene fragments with robust global optimization based …

We present a system for accurate real-time mapping of complex and arbitrary indoor scenes
in variable lighting conditions, using only a moving low-cost depth camera and commodity …

This paper is intended to pave the way for new researchers in the field of robotics and
autonomous systems, particularly those who are interested in robot localization and …

KinectFusion enables a user holding and moving a standard Kinect camera to rapidly create
detailed 3D reconstructions of an indoor scene. Only the depth data from Kinect is used to …