Autonomous driving vehicles require both a precise localization and mapping solution in
different driving environment. In this context, Simultaneous Localization and Mapping …

Visual SLAM (VSLAM) has been developing rapidly due to its advantages of low-cost
sensors, the easy fusion of other sensors, and richer environmental information. Traditional …

Dense simultaneous localization and mapping (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 shown encouraging results in various
domains, including promising progress in simultaneous localization and mapping (SLAM) …

We present Co-SLAM, a neural RGB-D SLAM system based on a hybrid representation, that
performs robust camera tracking and high-fidelity surface reconstruction in real time. Co …

We present the first application of 3D Gaussian Splatting in monocular SLAM the most
fundamental but the hardest setup for Visual SLAM. Our method which runs live at 3fps …

Neural volume rendering became increasingly popular recently due to its success in
synthesizing novel views of a scene from a sparse set of input images. So far, the geometry …

We introduce DROID-SLAM, a new deep learning based SLAM system. DROID-SLAM
consists of recurrent iterative updates of camera pose and pixelwise depth through a Dense …

We present a near real-time (10Hz) method for 6-DoF tracking of an unknown object from a
monocular RGBD video sequence, while simultaneously performing neural 3D …

Open-vocabulary scene understanding aims to localize and recognize unseen categories
beyond the annotated label space. The recent breakthrough of 2D open-vocabulary …