Building with Drones: Accurate 3D Facade Reconstruction using MAVs

Automatic reconstruction of 3D models from images using multi-view Structure-from-Motion methods has been one of the most fruitful outcomes of computer vision. These advances combined with the growing popularity of Micro Aerial Vehicles as an autonomous imaging platform, have made 3D vision tools ubiquitous for large number of Architecture, Engineering and Construction applications among audiences, mostly unskilled in computer vision. However, to obtain high-resolution and accurate reconstructions from a large-scale object using SfM, there are many critical constraints on the quality of image data, which often become sources of inaccuracy as the current 3D reconstruction pipelines do not facilitate the users to determine the fidelity of input data during the image acquisition. In this paper, we present and advocate a closed-loop interactive approach that performs incremental reconstruction in real-time and gives users an online feedback about the quality parameters like Ground Sampling Distance (GSD), image redundancy, etc on a surface mesh. We also propose a novel multi-scale camera network design to prevent scene drift caused by incremental map building, and release the first multi-scale image sequence dataset as a benchmark. Further, we evaluate our system on real outdoor scenes, and show that our interactive pipeline combined with a multi-scale camera network approach provides compelling accuracy in multi-view reconstruction tasks when compared against the state-of-the-art methods.Comment: 8 Pages, 2015 IEEE International Conference on Robotics and Automation (ICRA '15), Seattle, WA, US

Acute Beneficial Hemodynamic Effects of a Novel 3D-Echocardiographic Optimization Protocol in Cardiac Resynchronization Therapy

Post-implantation therapies to optimize cardiac resynchronization therapy (CRT) focus on adjustments of the atrio-ventricular (AV) delay and ventricular-to-ventricular (VV) interval. However, there is little consensus on how to achieve best resynchronization with these parameters. The aim of this study was to examine a novel combination of doppler echocardiography (DE) and three-dimensional echocardiography (3DE) for individualized optimization of device based AV delays and VV intervals compared to empiric programming.25 recipients of CRT (male: 56%, mean age: 67 years) were included in this study. Ejection fraction (EF), the primary outcome parameter, and left ventricular (LV) dimensions were evaluated by 3DE before CRT (baseline), after AV delay optimization while pacing the ventricles simultaneously (empiric VV interval programming) and after individualized VV interval optimization. For AV delay optimization aortic velocity time integral (AoVTI) was examined in eight different AV delays, and the AV delay with the highest AoVTI was programmed. For individualized VV interval optimization 3DE full-volume datasets of the left ventricle were obtained and analyzed to derive a systolic dyssynchrony index (SDI), calculated from the dispersion of time to minimal regional volume for all 16 LV segments. Consecutively, SDI was evaluated in six different VV intervals (including LV or right ventricular preactivation), and the VV interval with the lowest SDI was programmed (individualized optimization).EF increased from baseline 23±7% to 30±8 (p<0.001) after AV delay optimization and to 32±8% (p<0.05) after individualized optimization with an associated decrease of end-systolic volume from a baseline of 138±60 ml to 115±42 ml (p<0.001). Moreover, individualized optimization significantly reduced SDI from a baseline of 14.3±5.5% to 6.1±2.6% (p<0.001).Compared with empiric programming of biventricular pacemakers, individualized echocardiographic optimization with the integration of 3-dimensional indices into the optimization protocol acutely improved LV systolic function and decreased ESV and can be used to select the optimal AV delay and VV interval in CRT

Automatic Fusion of Partial Reconstructions

Novel image acquisition tools such as micro aerial vehicles (MAVs) in form of quad- or octo-rotor helicopters support the creation of 3D reconstructions with ground sampling distances below 1 cm. The limitation of aerial photogrammetry to nadir and oblique views in heights of several hundred meters is bypassed, allowing close-up photos of facades and ground features. However, the new acquisition modality also introduces challenges: First, flight space might be restricted in urban areas, which leads to missing views for accurate 3D reconstruction and causes fracturing of large models. This could also happen due to vegetation or simply a change of illumination during image acquisition. Second, accurate geo-referencing of reconstructions is difficult because of shadowed GPS signals in urban areas, so alignment based on GPS information is often not possible. <br><br> In this paper, we address the automatic fusion of such partial reconstructions. Our approach is largely based on the work of (Wendel et al., 2011a), but does not require an overhead digital surface model for fusion. Instead, we exploit that patch-based semi-dense reconstruction of the fractured model typically results in several point clouds covering overlapping areas, even if sparse feature correspondences cannot be established. We approximate orthographic depth maps for the individual parts and iteratively align them in a global coordinate system. As a result, we are able to generate point clouds which are visually more appealing and serve as an ideal basis for further processing. Mismatches between parts of the fused models depend only on the individual point density, which allows us to achieve a fusion accuracy in the range of &plusmn;1 cm on our evaluation dataset

Evaluations on multi-scale camera networks for precise and geo-accurate reconstructions from aerial and terrestrial images with user guidance

During the last decades photogrammetric computer vision systems have been well established in scien- tific and commercial applications. Recent developments in image-based 3D reconstruction systems have resulted in an easy way of creating realistic, visually appealing and accurate 3D models. We present a fully automated processing pipeline for metric and geo-accurate 3D reconstructions of complex geome- tries supported by an online feedback method for user guidance during image acquisition. Our approach is suited for seamlessly matching and integrating images with different scales, from different view points (aerial and terrestrial), and with different cameras into one single reconstruction. We evaluate our ap- proach based on different datasets for applications in mining, archaeology and urban environments and thus demonstrate the flexibility and high accuracy of our approach. Our evaluation includes accuracy related analyses investigating camera self-calibration, georegistration and camera network configuration

Analytical Cascades of Enzymes for Sensitive Detection of Structural Variations in Protein Samples

Protein function critically depends on structure. However, current analytical tools to monitor consistent higher-order structure with high sensitivity, as for instance required in the development of biopharmaceuticals, are limited. To complement existing assays, we present the analytical cascade of enzymes (ACE), a method based on enzymatic modifications of target proteins, which serve to exponentially amplify structural differences between them. The method enables conformational and chemical fingerprinting of closely related proteins, allowing for the sensitive detection of heterogeneities in protein preparations with high precision. Using this method, we detect protein variants differing in conformation only, as well as structural changes induced by diverse covalent modifications. Additionally, we employ this method to identify the nature of structural variants. Moreover, the ACE method should help to address the limited reproducibility in fundamental research, which partly relates to sample heterogeneities

Baseline characteristics.

<p>Values are shown as means ± standard deviation or count (percentage).</p><p>NYHA, New York Heart Association; CMP, cardiomyopathy; ACE, Angiotensin-converting enzyme; ARB, Angiotensin receptor blocker; LV, left ventricle; SDI, systolic dyssynchrony index.</p

Ventricular-to-ventricular (VV) intervals in patients with ischemic and dilated cardiomyopathy.

<p>Number of patients with simultaneous activation of left and right ventricle or sequential inter-ventricular pacing depending on ischemic or dilated cardiomyopathy.</p

Echocardiographic parameters at baseline and after AV delay and VV interval optimization.

<p>SDI, systolic dyssynchrony index; AV, atrio-ventricular; VV, ventriculo-ventricular; LV, left ventricular; VTI, velocity-time integral.</p><p>Shown are means ± standard deviation.</p>†<p>p<0.001: for comparison of AV optimization vs baseline.</p>¶<p>p<0.05: for comparison of complete optimization vs AV optimization only.</p><p>*p<0.05: for comparison of complete optimization vs baseline.</p