3D rekonstrukce na iOS

This bachelor thesis describes implementation of a real-time RGBD-based 3D reconstruction pipeline suited for Apple’s iPhone X with the TrueDepth camera. First, an overview of common approaches to the reconstruction problem is made, followed by a description of the underlying algorithms and techniques used in the thesis. Finally, the implementation details of the application pipeline are presented with performance overview of the implemented application.Tato bakalářská práce popisuje implementaci řetězce pro 3D rekonstrukci z RGBD snímků v reálném čase, určené pro Apple iPhone X s TrueDepth kamerou. Nejdříve je podán přehled běžných přístupů k rekonstrukci, následován popisem algoritmů a technik použitých v této práci. Nakonec jsou popsány implementační detaily zvoleného rekonstrukčního řetězce spolu s popisem výkonnosti implementované aplikace.460 - Katedra informatikyvýborn

Accelerated Object Tracking with Local Binary Features

Multi-object tracking is a problem with wide application in modern computing. Object tracking is leveraged in areas such as human computer interaction, autonomous vehicle navigation, panorama generation, as well as countless other robotic applications. Several trackers have demonstrated favorable results for tracking of single objects. However, modern object trackers must make significant tradeoffs in order to accommodate multiple objects while maintaining real-time performance. These tradeoffs include sacrifices in robustness and accuracy that adversely affect the results. This thesis details the design and multiple implementations of an object tracker that is focused on computational efficiency. The computational efficiency of the tracker is achieved through use of local binary descriptors in a template matching approach. Candidate templates are matched to a dictionary composed of both static and dynamic templates to allow for variation in the appearance of the object while minimizing the potential for drift in the tracker. Locality constraints have been used to reduce tracking jitter. Due to the significant promise for parallelization, the tracking algorithm was implemented on the Graphics Processing Unit (GPU) using the CUDA API. The tracker\u27s efficiency also led to its implantation on a mobile platform as one of the mobile trackers that can accurately track at faster than realtime speed. Benchmarks were performed to compare the proposed tracker to state of the art trackers on a wide range of standard test videos. The tracker implemented in this work has demonstrated a higher degree of accuracy while operating several orders of magnitude faster

Proceedings of the Linux Audio Conference 2018

These proceedings contain all papers presented at the Linux Audio Conference 2018. The conference took place at c-base, Berlin, from June 7th - 10th, 2018 and was organized in cooperation with the Electronic Music Studio at TU Berlin

Fusion of LIDAR with stereo camera data - an assessment

This thesis explores data fusion of LIDAR (laser range-finding) with stereo matching, with a particular emphasis on close-range industrial 3D imaging. Recently there has been interest in improving the robustness of stereo matching using data fusion with active range data. These range data have typically been acquired using time of flight cameras (ToFCs), however ToFCs offer poor spatial resolution and are noisy. Comparatively little work has been performed using LIDAR. It is argued that stereo and LIDAR are complementary and there are numerous advantages to integrating LIDAR into stereo systems. For instance, camera calibration is a necessary prerequisite for stereo 3D reconstruction, but the process is often tedious and requires precise calibration targets. It is shown that a visible-beam LIDAR enables automatic, accurate (sub-pixel) extrinsic and intrinsic camera calibration without any explicit targets. Two methods for using LIDAR to assist dense disparity maps from featureless scenes were investigated. The first involved using a LIDAR to provide high-confidence seed points for a region growing stereo matching algorithm. It is shown that these seed points allow dense matching in scenes which fail to match using stereo alone. Secondly, LIDAR was used to provide artificial texture in featureless image regions. Texture was generated by combining real or simulated images of every point the laser hits to form a pseudo-random pattern. Machine learning was used to determine the image regions that are most likely to be stereo- matched, reducing the number of LIDAR points required. Results are compared to competing techniques such as laser speckle, data projection and diffractive optical elements

Robotic Mapping and Localization with Real-Time Dense Stereo on Reconfigurable Hardware

A reconfigurable architecture for dense stereo is presented as an observation framework for a real-time implementation of the simultaneous localization and mapping problem in robotics. The reconfigurable sensor detects point features from stereo image pairs to use at the measurement update stage of the procedure. The main hardware blocks are a dense depth stereo accelerator, a left and right image corner detector, and a stage performing left-right consistency check. For the stereo-processor stage, we have implemented and tested a global-matching component based on a maximum-likelihood dynamic programming technique. The system includes a Nios II processor for data control and a USB 2.0 interface for host communication. Remote control is used to guide a vehicle equipped with a stereo head in an indoor environment. The FastSLAM Bayesian algorithm is applied in order to track and update observations and the robot path in real time. The system is assessed using real scene depth detection and public reference data sets. The paper also reports resource usage and a comparison of mapping and localization results with ground truth

A Phase Based Dense Stereo Algorithm Implemented in CUDA

Stereo imaging is routinely used in Simultaneous Localization and Mapping (SLAM) systems for the navigation and control of autonomous spacecraft proximity operations, advanced robotics, and robotic mapping and surveying applications. A key step (and generally the most computationally expensive step) in the generation of high fidelity geometric environment models from image data is the solution of the dense stereo correspondence problem. A novel method for solving the stereo correspondence problem to sub-pixel accuracy in the Fourier frequency domain by exploiting the Convolution Theorem is developed. The method is tailored to challenging aerospace applications by incorporation of correction factors for common error sources. Error-checking metrics verify correspondence matches to ensure high quality depth reconstructions are generated. The effect of geometric foreshortening caused by the baseline displacement of the cameras is modeled and corrected, drastically improving correspondence matching on highly off-normal surfaces. A metric for quantifying the strength of correspondence matches is developed and implemented to recognize and reject weak correspondences, and a separate cross-check verification provides a final defense against erroneous matches. The core components of this phase based dense stereo algorithm are implemented and optimized in the Compute Uni ed Device Architecture (CUDA) parallel computation environment onboard an NVIDIA Graphics Processing Unit (GPU). Accurate dense stereo correspondence matching is performed on stereo image pairs at a rate of nearly 10Hz

Real time motion estimation using a neural architecture implemented on GPUs

This work describes a neural network based architecture that represents and estimates object motion in videos. This architecture addresses multiple computer vision tasks such as image segmentation, object representation or characterization, motion analysis and tracking. The use of a neural network architecture allows for the simultaneous estimation of global and local motion and the representation of deformable objects. This architecture also avoids the problem of finding corresponding features while tracking moving objects. Due to the parallel nature of neural networks, the architecture has been implemented on GPUs that allows the system to meet a set of requirements such as: time constraints management, robustness, high processing speed and re-configurability. Experiments are presented that demonstrate the validity of our architecture to solve problems of mobile agents tracking and motion analysis