Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …

Rapid advances in 2D perception have led to systems that accurately detect objects in real-
world images. However, these systems make predictions in 2D, ignoring the 3D structure of …

Multi-view stereo reconstruction (MVS) in the wild requires to first estimate the camera
intrinsic and extrinsic parameters. These are usually tedious and cumbersome to obtain yet …

Simultaneous localization and mapping (SLAM) consists in the concurrent construction of a
model of the environment (the map), and the estimation of the state of the robot moving …

In this paper, we present a method for single image three-dimensional (3-D) cuboid object
detection and multiview object simultaneous localization and mapping in both static and …

Inspired by the recent success of methods that employ shape priors to achieve robust 3D
reconstructions, we propose a novel recurrent neural network architecture that we call the …

Implicit shape representations, such as Level Sets, provide a very elegant formulation for
performing computations involving curves and surfaces. However, including implicit …

For robots to navigate and interact more richly with the world around them, they will likely
require a deeper understanding of the world in which they operate. In robotics and related …

We present a learnt system for multi-view stereopsis. In contrast to recent learning based
methods for 3D reconstruction, we leverage the underlying 3D geometry of the problem …

Recently, Convolutional Neural Networks have shown promising results for 3D geometry
prediction. They can make predictions from very little input data such as a single color …