Rectification Strategies for a Binary Coded Structured Light 3D Scanner

Making a computer able to see exactly as a human being does was for many years one of the most interesting and challenging tasks involving lots of experts and pioneers in fields such as Computer Science and Artificial Intelligence. As a result, a whole field called Computer Vision has emerged becoming very soon a part of our daily life. The successful methodologies of this discipline have been applied in countless areas of application and their use is still in continuous expansion. On the other hand, in an increasing number of applications extracting information from simple 2D images is not enough and what is more requested instead is to use three-dimensional imaging techniques in order to reconstruct the 3D shape of the imaged objects and scene. The techniques developed in this context include both active systems, where some form of illumination is projected onto the scene, and passive systems, where the natural illumination of the scene is used. Among the active systems, one of the most reliable approaches for recovering the surface of objects is the use of structured light. This technique is based on projecting a light pattern and viewing the illuminated scene from one or more points of view. Since the pattern is coded, correspondences between image points and points of the projected pattern can be easily found. In particular, the performances of this kind of 3D scanner are determined by two key aspects, the accuracy and the acquisition time. This thesis aims to design and experiment some rectification strategies for a prototype of binary coded structured light 3D scanner. The rectification is a commonly used technique for stereo vision systems which, in case of structured light, facilitates the establishment of correspondences across a projected pattern and an acquired image and reduces the number of pattern images to be projected, resulting finally in a speeding-up of the acquisition times.Making a computer able to see exactly as a human being does was for many years one of the most interesting and challenging tasks involving lots of experts and pioneers in fields such as Computer Science and Artificial Intelligence. As a result, a whole field called Computer Vision has emerged becoming very soon a part of our daily life. The successful methodologies of this discipline have been applied in countless areas of application and their use is still in continuous expansion. On the other hand, in an increasing number of applications extracting information from simple 2D images is not enough and what is more requested instead is to use three-dimensional imaging techniques in order to reconstruct the 3D shape of the imaged objects and scene. The techniques developed in this context include both active systems, where some form of illumination is projected onto the scene, and passive systems, where the natural illumination of the scene is used. Among the active systems, one of the most reliable approaches for recovering the surface of objects is the use of structured light. This technique is based on projecting a light pattern and viewing the illuminated scene from one or more points of view. Since the pattern is coded, correspondences between image points and points of the projected pattern can be easily found. In particular, the performances of this kind of 3D scanner are determined by two key aspects, the accuracy and the acquisition time. This thesis aims to design and experiment some rectification strategies for a prototype of binary coded structured light 3D scanner. The rectification is a commonly used technique for stereo vision systems which, in case of structured light, facilitates the establishment of correspondences across a projected pattern and an acquired image and reduces the number of pattern images to be projected, resulting finally in a speeding-up of the acquisition times

Writing Reusable Digital Geometry Algorithms in a Generic Image Processing Framework

Digital Geometry software should reflect the generality of the underlying mathe- matics: mapping the latter to the former requires genericity. By designing generic solutions, one can effectively reuse digital geometry data structures and algorithms. We propose an image processing framework focused on the Generic Programming paradigm in which an algorithm on the paper can be turned into a single code, written once and usable with various input types. This approach enables users to design and implement new methods at a lower cost, try cross-domain experiments and help generalize resultsComment: Workshop on Applications of Discrete Geometry and Mathematical Morphology, Istanb : France (2010

Leveraging Deep Visual Descriptors for Hierarchical Efficient Localization

Many robotics applications require precise pose estimates despite operating in large and changing environments. This can be addressed by visual localization, using a pre-computed 3D model of the surroundings. The pose estimation then amounts to finding correspondences between 2D keypoints in a query image and 3D points in the model using local descriptors. However, computational power is often limited on robotic platforms, making this task challenging in large-scale environments. Binary feature descriptors significantly speed up this 2D-3D matching, and have become popular in the robotics community, but also strongly impair the robustness to perceptual aliasing and changes in viewpoint, illumination and scene structure. In this work, we propose to leverage recent advances in deep learning to perform an efficient hierarchical localization. We first localize at the map level using learned image-wide global descriptors, and subsequently estimate a precise pose from 2D-3D matches computed in the candidate places only. This restricts the local search and thus allows to efficiently exploit powerful non-binary descriptors usually dismissed on resource-constrained devices. Our approach results in state-of-the-art localization performance while running in real-time on a popular mobile platform, enabling new prospects for robotics research.Comment: CoRL 2018 Camera-ready (fix typos and update citations

Extracting 3D parametric curves from 2D images of Helical objects

Helical objects occur in medicine, biology, cosmetics, nanotechnology, and engineering. Extracting a 3D parametric curve from a 2D image of a helical object has many practical applications, in particular being able to extract metrics such as tortuosity, frequency, and pitch. We present a method that is able to straighten the image object and derive a robust 3D helical curve from peaks in the object boundary. The algorithm has a small number of stable parameters that require little tuning, and the curve is validated against both synthetic and real-world data. The results show that the extracted 3D curve comes within close Hausdorff distance to the ground truth, and has near identical tortuosity for helical objects with a circular profile. Parameter insensitivity and robustness against high levels of image noise are demonstrated thoroughly and quantitatively