Contour Generator Points for Threshold Selection and a Novel Photo-Consistency Measure for Space Carving

Space carving has emerged as a powerful method for multiview scene reconstruction. Although a wide variety of methods have been proposed, the quality of the reconstruction remains highly-dependent on the photometric consistency measure, and the threshold used to carve away voxels. In this paper, we present a novel photo-consistency measure that is motivated by a multiset variant of the chamfer distance. The new measure is robust to high amounts of within-view color variance and also takes into account the projection angles of back-projected pixels. Another critical issue in space carving is the selection of the photo-consistency threshold used to determine what surface voxels are kept or carved away. In this paper, a reliable threshold selection technique is proposed that examines the photo-consistency values at contour generator points. Contour generators are points that lie on both the surface of the object and the visual hull. To determine the threshold, a percentile ranking of the photo-consistency values of these generator points is used. This improved technique is applicable to a wide variety of photo-consistency measures, including the new measure presented in this paper. Also presented in this paper is a method to choose between photo-consistency measures, and voxel array resolutions prior to carving using receiver operating characteristic (ROC) curves

Deep Autoencoder for Combined Human Pose Estimation and body Model Upscaling

We present a method for simultaneously estimating 3D human pose and body shape from a sparse set of wide-baseline camera views. We train a symmetric convolutional autoencoder with a dual loss that enforces learning of a latent representation that encodes skeletal joint positions, and at the same time learns a deep representation of volumetric body shape. We harness the latter to up-scale input volumetric data by a factor of $4 \times$, whilst recovering a 3D estimate of joint positions with equal or greater accuracy than the state of the art. Inference runs in real-time (25 fps) and has the potential for passive human behaviour monitoring where there is a requirement for high fidelity estimation of human body shape and pose

Towards a cloud‑based automated surveillance system using wireless technologies

Cloud Computing can bring multiple benefits for Smart Cities. It permits the easy creation of centralized knowledge bases, thus straightforwardly enabling that multiple embedded systems (such as sensor or control devices) can have a collaborative, shared intelligence. In addition to this, thanks to its vast computing power, complex tasks can be done over low-spec devices just by offloading computation to the cloud, with the additional advantage of saving energy. In this work, cloud’s capabilities are exploited to implement and test a cloud-based surveillance system. Using a shared, 3D symbolic world model, different devices have a complete knowledge of all the elements, people and intruders in a certain open area or inside a building. The implementation of a volumetric, 3D, object-oriented, cloud-based world model (including semantic information) is novel as far as we know. Very simple devices (orange Pi) can send RGBD streams (using kinect cameras) to the cloud, where all the processing is distributed and done thanks to its inherent scalability. A proof-of-concept experiment is done in this paper in a testing lab with multiple cameras connected to the cloud with 802.11ac wireless technology. Our results show that this kind of surveillance system is possible currently, and that trends indicate that it can be improved at a short term to produce high performance vigilance system using low-speed devices. In addition, this proof-of-concept claims that many interesting opportunities and challenges arise, for example, when mobile watch robots and fixed cameras would act as a team for carrying out complex collaborative surveillance strategies.Ministerio de Economía y Competitividad TEC2016-77785-PJunta de Andalucía P12-TIC-130

Marching Intersections: An Efficient Approach to Shape-from-Silhouette

A new shape-from-silhouette algorithm for the creation of 3D digital models is presented. The algorithm is based on the use of the Marching Intersection (MI) data structure, a volumetric scheme which allows ef\ufb01cient representation of 3D polyhedra and reduces the boolean operations between them to simple boolean operations on linear intervals. MI supports the de\ufb01nition of a direct shape-from-silhouette approach: the 3D conoids built from the silhouettes extracted from the images of the object are directly intersected to form the resulting 3D digital model. Compared to existing methods, our approach allows high quality models to be obtained in an ef\ufb01cient way. Examples on synthetic objects together with quantitative and qualitative evaluations are given

Progressive 3D reconstruction of unknown objects using one eye-in-hand camera

Proceedings of: 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO 2009) December 19-23, 2009, Guilin, ChinaThis paper presents a complete 3D-reconstruction method optimized for online object modeling in the context of object grasping by a robot hand. The proposed solution is based on images captured by an eye-in-hand camera mounted on the robot arm and is an original combination of classical but simplified reconstruction methods. The different techniques used form a process that offers fast, progressive and reactive reconstruction of the object.European Community's Seventh Framework ProgramThe research leading to these results has been partially supported by the HANDLE project, which has received funding from the European Communitity’s Seventh Framework Programme (FP7/2007-2013) under grant agreement ICT 23164