Cross-calibration of Time-of-flight and Colour Cameras

Time-of-flight cameras provide depth information, which is complementary to the photometric appearance of the scene in ordinary images. It is desirable to merge the depth and colour information, in order to obtain a coherent scene representation. However, the individual cameras will have different viewpoints, resolutions and fields of view, which means that they must be mutually calibrated. This paper presents a geometric framework for this multi-view and multi-modal calibration problem. It is shown that three-dimensional projective transformations can be used to align depth and parallax-based representations of the scene, with or without Euclidean reconstruction. A new evaluation procedure is also developed; this allows the reprojection error to be decomposed into calibration and sensor-dependent components. The complete approach is demonstrated on a network of three time-of-flight and six colour cameras. The applications of such a system, to a range of automatic scene-interpretation problems, are discussed.Comment: 18 pages, 12 figures, 3 table

Depth Fields: Extending Light Field Techniques to Time-of-Flight Imaging

A variety of techniques such as light field, structured illumination, and time-of-flight (TOF) are commonly used for depth acquisition in consumer imaging, robotics and many other applications. Unfortunately, each technique suffers from its individual limitations preventing robust depth sensing. In this paper, we explore the strengths and weaknesses of combining light field and time-of-flight imaging, particularly the feasibility of an on-chip implementation as a single hybrid depth sensor. We refer to this combination as depth field imaging. Depth fields combine light field advantages such as synthetic aperture refocusing with TOF imaging advantages such as high depth resolution and coded signal processing to resolve multipath interference. We show applications including synthesizing virtual apertures for TOF imaging, improved depth mapping through partial and scattering occluders, and single frequency TOF phase unwrapping. Utilizing space, angle, and temporal coding, depth fields can improve depth sensing in the wild and generate new insights into the dimensions of light's plenoptic function.Comment: 9 pages, 8 figures, Accepted to 3DV 201

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions

Kinect Range Sensing: Structured-Light versus Time-of-Flight Kinect

Recently, the new Kinect One has been issued by Microsoft, providing the next generation of real-time range sensing devices based on the Time-of-Flight (ToF) principle. As the first Kinect version was using a structured light approach, one would expect various differences in the characteristics of the range data delivered by both devices. This paper presents a detailed and in-depth comparison between both devices. In order to conduct the comparison, we propose a framework of seven different experimental setups, which is a generic basis for evaluating range cameras such as Kinect. The experiments have been designed with the goal to capture individual effects of the Kinect devices as isolatedly as possible and in a way, that they can also be adopted, in order to apply them to any other range sensing device. The overall goal of this paper is to provide a solid insight into the pros and cons of either device. Thus, scientists that are interested in using Kinect range sensing cameras in their specific application scenario can directly assess the expected, specific benefits and potential problem of either device.Comment: 58 pages, 23 figures. Accepted for publication in Computer Vision and Image Understanding (CVIU

Reliable fusion of ToF and stereo depth driven by confidence measures

In this paper we propose a framework for the fusion of depth data produced by a Time-of-Flight (ToF) camera and stereo vision system. Initially, depth data acquired by the ToF camera are upsampled by an ad-hoc algorithm based on image segmentation and bilateral filtering. In parallel a dense disparity map is obtained using the Semi- Global Matching stereo algorithm. Reliable confidence measures are extracted for both the ToF and stereo depth data. In particular, ToF confidence also accounts for the mixed-pixel effect and the stereo confidence accounts for the relationship between the pointwise matching costs and the cost obtained by the semi-global optimization. Finally, the two depth maps are synergically fused by enforcing the local consistency of depth data accounting for the confidence of the two data sources at each location. Experimental results clearly show that the proposed method produces accurate high resolution depth maps and outperforms the compared fusion algorithms

Robust Intrinsic and Extrinsic Calibration of RGB-D Cameras

Color-depth cameras (RGB-D cameras) have become the primary sensors in most robotics systems, from service robotics to industrial robotics applications. Typical consumer-grade RGB-D cameras are provided with a coarse intrinsic and extrinsic calibration that generally does not meet the accuracy requirements needed by many robotics applications (e.g., highly accurate 3D environment reconstruction and mapping, high precision object recognition and localization, ...). In this paper, we propose a human-friendly, reliable and accurate calibration framework that enables to easily estimate both the intrinsic and extrinsic parameters of a general color-depth sensor couple. Our approach is based on a novel two components error model. This model unifies the error sources of RGB-D pairs based on different technologies, such as structured-light 3D cameras and time-of-flight cameras. Our method provides some important advantages compared to other state-of-the-art systems: it is general (i.e., well suited for different types of sensors), based on an easy and stable calibration protocol, provides a greater calibration accuracy, and has been implemented within the ROS robotics framework. We report detailed experimental validations and performance comparisons to support our statements

SR-4000 and CamCube3.0 Time of Flight (ToF) Cameras: Tests and Comparison

In this paper experimental comparisons between two Time-of-Flight (ToF) cameras are reported in order to test their performance and to give some procedures for testing data delivered by this kind of technology. In particular, the SR-4000 camera by Mesa Imaging AG and the CamCube3.0 by PMD Technologies have been evaluated since they have good performances and are well known to researchers dealing with Time-of- Flight (ToF) cameras. After a brief overview of commercial ToF cameras available on the market and the main specifications of the tested devices, two topics are presented in this paper. First, the influence of camera warm-up on distance measurement is analyzed: a warm-up of 40 minutes is suggested to obtain the measurement stability, especially in the case of the CamCube3.0 camera, that exhibits distance measurement variations of several centimeters. Secondly, the variation of distance measurement precision variation over integration time is presented: distance measurement precisions of some millimeters are obtained in both cases. Finally, a comparison between the two cameras based on the experiments and some information about future work on evaluation of sunlight influence on distance measurements are reporte

Método para el registro automático de imágenes basado en transformaciones proyectivas planas dependientes de las distancias y orientado a imágenes sin características comunes

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Físicas, Departamento de Arquitectura de Computadores y Automática, leída el 18-12-2015Multisensory data fusion oriented to image-based application improves the accuracy, quality and availability of the data, and consequently, the performance of robotic systems, by means of combining the information of a scene acquired from multiple and different sources into a unified representation of the 3D world scene, which is more enlightening and enriching for the subsequent image processing, improving either the reliability by using the redundant information, or the capability by taking advantage of complementary information. Image registration is one of the most relevant steps in image fusion techniques. This procedure aims the geometrical alignment of two or more images. Normally, this process relies on feature-matching techniques, which is a drawback for combining sensors that are not able to deliver common features. For instance, in the combination of ToF and RGB cameras, the robust feature-matching is not reliable. Typically, the fusion of these two sensors has been addressed from the computation of the cameras calibration parameters for coordinate transformation between them. As a result, a low resolution colour depth map is provided. For improving the resolution of these maps and reducing the loss of colour information, extrapolation techniques are adopted. A crucial issue for computing high quality and accurate dense maps is the presence of noise in the depth measurement from the ToF camera, which is normally reduced by means of sensor calibration and filtering techniques. However, the filtering methods, implemented for the data extrapolation and denoising, usually over-smooth the data, reducing consequently the accuracy of the registration procedure...La fusión multisensorial orientada a aplicaciones de procesamiento de imágenes, conocida como fusión de imágenes, es una técnica que permite mejorar la exactitud, la calidad y la disponibilidad de datos de un entorno tridimensional, que a su vez permite mejorar el rendimiento y la operatividad de sistemas robóticos. Dicha fusión, se consigue mediante la combinación de la información adquirida por múltiples y diversas fuentes de captura de datos, la cual se agrupa del tal forma que se obtiene una mejor representación del entorno 3D, que es mucho más ilustrativa y enriquecedora para la implementación de métodos de procesamiento de imágenes. Con ello se consigue una mejora en la fiabilidad y capacidad del sistema, empleando la información redundante que ha sido adquirida por múltiples sensores. El registro de imágenes es uno de los procedimientos más importantes que componen la fusión de imágenes. El objetivo principal del registro de imágenes es la consecución de la alineación geométrica entre dos o más imágenes. Normalmente, este proceso depende de técnicas de búsqueda de patrones comunes entre imágenes, lo cual puede ser un inconveniente cuando se combinan sensores que no proporcionan datos con características similares. Un ejemplo de ello, es la fusión de cámaras de color de alta resolución (RGB) con cámaras de Tiempo de Vuelo de baja resolución (Time-of-Flight (ToF)), con las cuales no es posible conseguir una detección robusta de patrones comunes entre las imágenes capturadas por ambos sensores. Por lo general, la fusión entre estas cámaras se realiza mediante el cálculo de los parámetros de calibración de las mismas, que permiten realizar la trasformación homogénea entre ellas. Y como resultado de este xii Abstract procedimiento, se obtienen mapas de profundad y de color de baja resolución. Con el objetivo de mejorar la resolución de estos mapas y de evitar la pérdida de información de color, se utilizan diversas técnicas de extrapolación de datos. Un factor crucial a tomar en cuenta para la obtención de mapas de alta calidad y alta exactitud, es la presencia de ruido en las medidas de profundidad obtenidas por las cámaras ToF. Este problema, normalmente se reduce mediante la calibración de estos sensores y con técnicas de filtrado de datos. Sin embargo, las técnicas de filtrado utilizadas, tanto para la interpolación de datos, como para la reducción del ruido, suelen producir el sobre-alisamiento de los datos originales, lo cual reduce la exactitud del registro de imágenes...Sección Deptal. de Arquitectura de Computadores y Automática (Físicas)Fac. de Ciencias FísicasTRUEunpu

Image-guided ToF depth upsampling: a survey

Recently, there has been remarkable growth of interest in the development and applications of time-of-flight (ToF) depth cameras. Despite the permanent improvement of their characteristics, the practical applicability of ToF cameras is still limited by low resolution and quality of depth measurements. This has motivated many researchers to combine ToF cameras with other sensors in order to enhance and upsample depth images. In this paper, we review the approaches that couple ToF depth images with high-resolution optical images. Other classes of upsampling methods are also briefly discussed. Finally, we provide an overview of performance evaluation tests presented in the related studies