We present a novel approach to real-time dense visual simultaneous localisation and
mapping. Our system is capable of capturing comprehensive dense globally consistent …

We present MaskFusion, a real-time, object-aware, semantic and dynamic RGB-D SLAM
system that goes beyond traditional systems which output a purely geometric map of a static …

We present the first dense SLAM system capable of reconstructing non-rigidly deforming
scenes in real-time, by fusing together RGBD scans captured from commodity sensors. Our …

We present a novel approach to real-time dense visual SLAM. Our system is capable of
capturing comprehensive dense globally consistent surfel-based maps of room scale …

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 …

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 new simultaneous localization and mapping (SLAM) system capable of
producing high-quality globally consistent surface reconstructions over hundreds of meters …

We introduce MIPS-Fusion, a robust and scalable online RGB-D reconstruction method
based on a novel neural implicit representation-multi-implicit-submap. Different from existing …

Volumetric methods provide efficient, flexible and simple ways of integrating multiple depth
images into a full 3D model. They provide dense and photorealistic 3D reconstructions, and …

In this paper we introduce Co-Fusion, a dense SLAM system that takes a live stream of RGB-
D images as input and segments the scene into different objects (using either motion or …