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Non-Rigid Structure-from-Motion (NRSfM) reconstructs a deformable 3D object from the correspondences established between monocular 2D images. Current NRSfM methods lack statistical robustness, which is the ability to cope with correspondence errors.This prevents one to use automatically established correspondences, which are prone to errors, thereby strongly limiting the scope of NRSfM. We propose a three-step automatic pipeline to solve NRSfM robustly by exploiting isometry. Step 1 computes the optical flow from correspondences, step 2 reconstructs each 3D point's normal vector using multiple reference images and integrates them to form surfaces with the best reference and step 3 rejects the 3D points that break isometry in their local neighborhood. Importantly, each step is designed to discard or flag erroneous correspondences. Our contributions include the robustification of optical flow by warp estimation, new fast analytic solutions to local normal reconstruction and their robustification, and a new scale-independent measure of 3D local isometric coherence. Experimental results show that our robust NRSfM method consistently outperforms existing methods on both synthetic and real datasets., Accepted in TPAMI 2021

International audience; Shape-from-Template (SfT) solves the registration and 3D reconstruction of a deformable 3D object, represented by the template, from a single image. Recently, methods based on deep learning have been able to solve SfT for the wide-baseline case in real-time, clearly surpassing classical methods. However, the main limitation of current methods is the need for fine tuning of the neural models to a specific geometry and appearance represented by the template texture map. We propose the first texture-generic deep learning SfT method which adapts to new texture maps at run-time, without the need for texture specific fine tuning. We achieve this by dividing the problem into a segmentation step and a registration and reconstruction step, both solved with deep learning. We include the template texture map as one of the neural inputs in both steps, training our models to adapt to different ones. We show that our method obtains comparable or better results to previous deep learning models, which are texture specific. It works in challenging imaging conditions, including complex deformations, occlusions, motion blur and poor textures. Our implementation runs in real-time, with a low-cost GPU and CPU.

3D reconstruction of deformable objects using inter-image visual motion from monocular images has been studied under Shape-from-Template (SfT) and Non-Rigid Structure-from-Motion (NRSfM). Most methods have been developed for simple deformation models, primarily isometry. They may treat a surface as a discrete set of points and draw constraints from the points only or they may use a non-parametric representation and use both points and differentials to express constraints. We propose a differential framework based on Cartan's theory of connections and moving frames. It is applicable to SfT and NRSfM, and to deformation models other than isometry. It utilises infinitesimal-level assumptions on the surface's geometry and mappings. It has the following properties. 1) It allows one to derive existing solutions in a simpler way. 2) It models SfT and NRSfM in a unified way. 3) It allows us to introduce a new skewless deformation model and solve SfT and NRSfM for it. 4) It facilitates a generic solution to SfT which does not require deformation modeling. Our framework is complete: it solves deformable 3D reconstruction for a whole class of algebraic deformation models including isometry. We compared our solutions with the state-of-the-art methods and show that ours outperform in terms of both accuracy and computation time.

Measuring the Time delay of Arrival (TDOA) between a set of sensors is the basic setup for many applications, such as localization or signal beamforming. This paper presents the set of TDOA matrices, which are built from noise-free TDOA measurements, not requiring knowledge of the sensor array geometry. We prove that TDOA matrices are rank-two and have a special SVD decomposition that leads to a compact linear parametric representation. Properties of TDOA matrices are applied in this paper to perform denoising, by finding the TDOA matrix closest to the matrix composed with noisy measurements. The paper shows that this problem admits a closed-form solution for TDOA measurements contaminated with Gaussian noise which extends to the case of having missing data. The paper also proposes a novel robust denoising method resistant to outliers, missing data and inspired in recent advances in robust low-rank estimation. Experiments in synthetic and real datasets show significant improvements of the proposed denoising algorithms in TDOA-based localization, both in terms of TDOA accuracy estimation and localization error.

Employment interviews are relevant scenarios for the study of social interaction. In this setting, social skills play an important role, even though the interactions between potential employers and candidates are often limited. One fundamental aspect of social interaction is the use of nonverbal communication , which affects how we are socially perceived. We present a method to automatically extract body communicative cues from one-on-one conversations recorded with Kinect devices. First, we find the three-dimensional position of hands and head of the subject, and, aided by training data, we infer the upper body pose. Then, we use the inferred poses to perform action recognition and build person-specific activity descriptors. We evaluate our system with both domain-specific and public, generic datasets, and show competitive performance.

We study a problem that we call Shape-from-Template , which is the problem of reconstructing the shape of a deformable surface from a single image and a 3D template. Current methods in the literature address the case of isometric deformations, and relax the isometry constraint to the convex inextensibility constraint, solved using the so-called maximum depth heuristic. We call these methods zeroth-order since they use image point locations (the zeroth-order differential structure) to solve the shape inference problem from a perspective image. We propose a novel class of methods that we call first-order. The key idea is to use both image point locations and their first-order differential structure. The latter can be easily extracted from a warp between the template and the input image. We give a unified problem formulation as a system of PDEs for isometric and conformal surfaces that we solve analytically . This has important consequences. First, it gives the first analytical algorithms to solve this type of reconstruction problems. Second, it gives the first algorithms to solve for the exact constraints . Third, it allows us to study the well-posedness of this type of reconstruction: we establish that isometric surfaces can be reconstructed unambiguously and that conformal surfaces can be reconstructed up to a few discrete ambiguities and a global scale. In the latter case, the candidate solution surfaces are obtained analytically. Experimental results on simulated and real data show that our isometric methods generally perform as well as or outperform state of the art approaches in terms of reconstruction accuracy, while our conformal methods largely outperform all isometric methods for extensible deformations.

This paper presents a current control scheme for a voltage-source converter connected to the grid through an LCL filter. The proposed current control is based on the linear quadratic (LQ) Gaussian (LQG) servo controller technique, which combines the LQ regulator and the Kalman filter (KF). The use of the KF instead of a Luenberger estimator improves the quality of the estimation of state variables in noisy environments, increasing the noise immunity of the control loop. This paper focuses on giving some criteria and practical hints to choose both the KF and LQ design parameters to achieve null steady-state tracking error, channel decoupling, and noise immunity. Furthermore, this paper analyzes the steady-state KF approximation that allows a significant decrement in the computational complexity of the algorithm at nearly no cost in accuracy. Simulation and experimental results prove the correct operation of the proposed LQG current control and the possible advantages of using KF to estimate the state vector.

This paper proposes a grid impedance monitoring system for distributed power generation electronic interfaces. The system estimates the grid equivalent impedance and voltage source from the voltage measurements performed at the point of common coupling. The estimation algorithm is based on a recursive least-squares algorithm implemented in the complex field. Simultaneously, the system evaluates the quality of the estimation, minimizing its influence on the grid and detecting islanding situations. The proposed system performance has been evaluated under experimental testing.

In this paper, a system capable of obtaining the 3-D pose of a mobile robot using a ring of calibrated cameras attached to the environment is proposed. The system robustly tracks point fiducials in the image plane of the set of cameras generated by the robot's rigid shape in motion. Each fiducial is identified with a point belonging to a sparse 3-D geometrical model of the robot's structure. Such a model allows direct pose estimation from image measurements, and it can easily be enriched at each iteration with new points as the robot motion evolves. The process is divided into an initialization step, where the structure of the robot is obtained, and an online step, which is solved using sequential Bayesian inference. The approach allows the proper modeling of uncertainty in measurements and estimations, and at the same time, it serves as a regularization step in pose estimation. The proposed system is verified using simulated and real data.