7 accepted papers
We propose a novel learning-based method for multi-view stereo (MVS) depth estimation capable of recovering depth from images taken from known, but unconstrained, views. Existing MVS methods extract features from each image independently before projecting them onto a set of planes at candidate depth…
Although deep neural networks have achieved state-of-the-art performance for stereo depth estimation, they can suffer from a significant drop in accuracy when tested on images from novel domains. Recent work has shown that self-supervised online adaptation is a promising approach for closing this pe…
Accurate rotation estimation is at the heart of robot perception tasks such as visual odometry and object pose estimation. Deep neural networks have provided a new way to perform these tasks, and the choice of rotation representation is an important part of network design. In this work, we present a…
We propose an efficient method for monocular simultaneous localization and mapping (SLAM) that is capable of estimating metrically-scaled motion without additional sensors or hardware acceleration by integrating metric depth predictions from a neural network into a geometric SLAM factor graph. Unlik…
We propose a lightweight method for dense online monocular depth estimation capable of reconstructing 3D meshes on computationally constrained platforms. Our main contribution is to pose the reconstruction problem as a non-local variational optimization over a time-varying Delaunay graph of the scen…
We present a method for Simultaneous Localization and Mapping (SLAM) using a monocular camera that is capable of reconstructing dense 3D geometry online without the aid of a graphics processing unit (GPU). Our key contribution is a multi-resolution depth estimation and spatial smoothing process that
We propose a method of real-time monocular camera-based localization in known environments. With the goal of controlling high-speed micro air vehicles (MAVs), we localize with respect to a mesh map of the environment that can support both pose estimation and trajectory planning. Using only limited h