Photometric Depth Super-Resolution

This study explores the use of photometric techniques (shape-from-shading and uncalibrated photometric stereo) for upsampling the low-resolution depth map from an RGB-D sensor to the higher resolution of the companion RGB image. A single-shot variational approach is first put forward, which is effective as long as the target's reflectance is piecewise-constant. It is then shown that this dependency upon a specific reflectance model can be relaxed by focusing on a specific class of objects (e.g., faces), and delegate reflectance estimation to a deep neural network. A multi-shot strategy based on randomly varying lighting conditions is eventually discussed. It requires no training or prior on the reflectance, yet this comes at the price of a dedicated acquisition setup. Both quantitative and qualitative evaluations illustrate the effectiveness of the proposed methods on synthetic and real-world scenarios.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2019. First three authors contribute equall

Hierarchical structure-and-motion recovery from uncalibrated images

This paper addresses the structure-and-motion problem, that requires to find camera motion and 3D struc- ture from point matches. A new pipeline, dubbed Samantha, is presented, that departs from the prevailing sequential paradigm and embraces instead a hierarchical approach. This method has several advantages, like a provably lower computational complexity, which is necessary to achieve true scalability, and better error containment, leading to more stability and less drift. Moreover, a practical autocalibration procedure allows to process images without ancillary information. Experiments with real data assess the accuracy and the computational efficiency of the method.Comment: Accepted for publication in CVI

What can be done with an embedded stereo-rig in urban environments?

International audienceThe development of the Autonomous Guided Vehicles (AGVs) with urban applications are now possible due to the recent solutions (DARPA Grand Challenge) developed to solve the Simultaneous Localization And Mapping (SLAM) problem: perception, path planning and control. For the last decade, the introduction of GPS systems and vision have been allowed the transposition of SLAM methods dedicated to indoor environments to outdoor ones. When the GPS data are unavailable, the current position of the mobile robot can be estimated by the fusion of data from odometer and/or Inertial Navigation System (INS). We detail in this article what can be done with an uncalibrated stereo-rig, when it is embedded in a vehicle which is going through urban roads. The methodology is based on features extracted on planes: we mainly assume the road at the foreground as the plane common to all the urban scenes but other planes like vertical frontages of buildings can be used if the features extracted on the road are not enough relevant. The relative motions of the coplanar features tracked with both cameras allow us to stimate the vehicle ego-motion with a high precision. Futhermore, the features which don't check the relative motion of the considered plane can be assumed as obstacles

Contribution towards a fast stereo dense matching.

Stereo matching is important in the area of computer vision as it is the basis of the reconstruction process. Many applications require 3D reconstruction such as view synthesis, robotics... The main task of matching uncalibrated images is to determine the corresponding pixels and other features where the motion between these images and the camera parameters is unknown. Although some methods have been carried out over the past two decades on the matching problem, most of these methods are not practical and difficult to implement. Our approach considers a reliable image edge features in order to develop a fast and practical method. Therefore, we propose a fast stereo matching algorithm combining two different approaches for matching as the image is segmented into two sets of regions: edge regions and non-edge regions. We have used an algebraic method that preserves disparity continuity at the object continuous surfaces. Our results demonstrate that we gain a speed dense matching while the implementation is kept simple and straightforward.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .Z42. Source: Masters Abstracts International, Volume: 44-03, page: 1420. Thesis (M.Sc.)--University of Windsor (Canada), 2005

Viewfinder: final activity report

The VIEW-FINDER project (2006-2009) is an 'Advanced Robotics' project that seeks to apply a semi-autonomous robotic system to inspect ground safety in the event of a fire. Its primary aim is to gather data (visual and chemical) in order to assist rescue personnel. A base station combines the gathered information with information retrieved from off-site sources. The project addresses key issues related to map building and reconstruction, interfacing local command information with external sources, human-robot interfaces and semi-autonomous robot navigation. The VIEW-FINDER system is a semi-autonomous; the individual robot-sensors operate autonomously within the limits of the task assigned to them, that is, they will autonomously navigate through and inspect an area. Human operators monitor their operations and send high level task requests as well as low level commands through the interface to any nodes in the entire system. The human interface has to ensure the human supervisor and human interveners are provided a reduced but good and relevant overview of the ground and the robots and human rescue workers therein