Acceleration of stereo-matching on multi-core CPU and GPU

This paper presents an accelerated version of a dense stereo-correspondence algorithm for two different parallelism enabled architectures, multi-core CPU and GPU. The algorithm is part of the vision system developed for a binocular robot-head in the context of the CloPeMa 1 research project. This research project focuses on the conception of a new clothes folding robot with real-time and high resolution requirements for the vision system. The performance analysis shows that the parallelised stereo-matching algorithm has been significantly accelerated, maintaining 12x and 176x speed-up respectively for multi-core CPU and GPU, compared with non-SIMD singlethread CPU. To analyse the origin of the speed-up and gain deeper understanding about the choice of the optimal hardware, the algorithm was broken into key sub-tasks and the performance was tested for four different hardware architectures

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

Acceleration of stereo-matching on multi-core CPU and GPU

This paper presents an accelerated version of a dense stereo-correspondence algorithm for two different parallelism enabled architectures, multi-core CPU and GPU. The algorithm is part of the vision system developed for a binocular robot-head in the context of the CloPeMa 1 research project. This research project focuses on the conception of a new clothes folding robot with real-time and high resolution requirements for the vision system. The performance analysis shows that the parallelised stereo-matching algorithm has been significantly accelerated, maintaining 12x and 176x speed-up respectively for multi-core CPU and GPU, compared with non-SIMD singlethread CPU. To analyse the origin of the speed-up and gain deeper understanding about the choice of the optimal hardware, the algorithm was broken into key sub-tasks and the performance was tested for four different hardware architectures

Stereo Matching Using a Modified Efficient Belief Propagation in a Level Set Framework

Stereo matching determines correspondence between pixels in two or more images of the same scene taken from different angles; this can be handled either locally or globally. The two most common global approaches are belief propagation (BP) and graph cuts. Efficient belief propagation (EBP), which is the most widely used BP approach, uses a multi-scale message passing strategy, an O(k) smoothness cost algorithm, and a bipartite message passing strategy to speed up the convergence of the standard BP approach. As in standard belief propagation, every pixel sends messages to and receives messages from its four neighboring pixels in EBP. Each outgoing message is the sum of the data cost, incoming messages from all the neighbors except the intended receiver, and the smoothness cost. Upon convergence, the location of the minimum of the final belief vector is defined as the current pixel’s disparity. The present effort makes three main contributions: (a) it incorporates level set concepts, (b) it develops a modified data cost to encourage matching of intervals, (c) it adjusts the location of the minimum of outgoing messages for select pixels that is consistent with the level set method. When comparing the results of the current work with that of standard EBP, the disparity results are very similar, as they should be

Ricostruzione 3D con sensore a tempo di volo e telecamere stereo

Questo lavoro di tesi verte alla realizzazione di un software per la ricostruzione 3D di una scena utilizzando tipi diversi di sensori. Nel caso specifico, il sistema di acquisizione è composto da una coppia di telecamere stereo e un sensore a tempo di volo. Lo scopo è fornire una ricostruzione più precisa combinando l'informazione data dai singoli sensori, cercando di sfruttare i loro vantaggi e attenuando il più possibile gli svantaggi. La scelta dei sensori gioca quindi un ruolo fondamentale: in base alle loro caratteristiche, le telecamere stereo e il sensore a tempo di volo tendono ad essere complementari, dimostrandosi una buona combinazione. Il framework di ricostruzione 3D sviluppato può essere esteso facilmente a più di due sensori. La tesi è strutturata nei seguenti capitoli fondamentali: la descrizione della tecnologia dei sensori, compresi i loro vantaggi e svantaggi; la descrizione dei metodi di ricostruzione 3D associati ai due sensori con la relativa stima di affidabilità; l'unione delle informazioni basandosi sulla stima dell'affidabilità; infine, viene presentata l'analisi dei risultati ottenuti alla fine del progett

Fast locally consistent dense stereo on multicore

Many computer vision applications require fast and accurate 3D measurements. However, despite the advent of powerful computing architectures (e.g., multicore CPU and GPU), most top-ranked dense stereo algorithms rely on global 2D disparity optimization methods that are often too slow for practical use. Moreover, their huge memory requirements are typically not suited to devices with constrained resources (e.g., FPGA). Nevertheless, algorithms based on 1D disparity optimization methods (\u131.e., Dynamic Programming and Scanline Optimization) provide a good trade-off between accuracy and efficiency with a limited memory footprint. In this paper, we show that enforcing a relaxed local consistency constraint to the disparity fields, provided by fast 1D disparity optimization methods, yields much more rapidly, results comparable to those of the top ranked approaches. The simple and non-iterative computational structure of our proposal enables us to exploit coarse grained parallelism on multicore CPUs. Moreover, due to its limited memory footprint, our proposal could be potentially mapped on devices, such as FPGA, with constrained resources

Efficient and accurate stereo matching for cloth manipulation

Due to the recent development of robotic techniques, researching robots that can assist in everyday household tasks, especially robotic cloth manipulation has become popular in recent years. Stereo matching forms a crucial part of the robotic vision and aims to derive depth information from image pairs captured by the stereo cameras. Although stereo robotic vision is widely adopted for cloth manipulation robots in the research community, this remains a challenging research task. Robotic vision requires very accurate depth output in a relatively short timespan in order to successfully perform cloth manipulation in real-time. In this thesis, we mainly aim to develop a robotic stereo matching based vision system that is both efficient and effective for the task of robotic cloth manipulation. Effectiveness refers to the accuracy of the depth map generated from the stereo matching algorithms for the robot to grasp the required details to achieve the given task on cloth materials while efficiency emphasizes the required time for the stereo matching to process the images. With respect to efficiency, firstly, by exploring a variety of different hardware architectures such as multi-core CPU and graphic processors (GPU) to accelerate stereo matching, we demonstrate that the parallelised stereo-matching algorithm can be significantly accelerated, achieving 12X and 176X speed-ups respectively for multi-core CPU and GPU, compared with SISD (Single Instruction, Single Data) single-thread CPU. In terms of effectiveness, due to the fact that there are no cloth based testbeds with depth map ground-truths for evaluating the accuracy of stereo matching performance in this context, we created five different testbeds to facilitate evaluation of stereo matching in the context of cloth manipulation. In addition, we adapted a guided filtering algorithm into a pyramidical stereo matching framework that works directly for unrectified images, and evaluate its accuracy utilizing the created cloth testbeds. We demonstrate that our proposed approach is not only efficient, but also accurate and suits well to the characteristics of the task of cloth manipulations. This also shows that rather than relying on image rectification, directly applying stereo matching to unrectified images is effective and efficient. Finally, we further explore whether we can improve efficiency while maintaining reasonable accuracy for robotic cloth manipulations (i.e.~trading off accuracy for efficiency). We use a foveated matching algorithm, inspired by biological vision systems, and found that it is effective in trading off accuracy for efficiency, achieving almost the same level of accuracy for both cloth grasping and flattening tasks with two to three fold acceleration. We also demonstrate that with the robot we can use machine learning techniques to predict the optimal foveation level in order to accomplish the robotic cloth manipulation tasks successfully and much more efficiently. To summarize, in this thesis, we extensively study stereo matching, contributing to the long-term goal of developing effective ways for efficient whilst accurate robotic stereo matching for cloth manipulation

3D data fusion from multiple sensors and its applications

The introduction of depth cameras in the mass market contributed to make computer vision applicable to many real world applications, such as human interaction in virtual environments, autonomous driving, robotics and 3D reconstruction. All these problems were originally tackled by means of standard cameras, but the intrinsic ambiguity in the bidimensional images led to the development of depth cameras technologies. Stereo vision was first introduced to provide an estimate of the 3D geometry of the scene. Structured light depth cameras were developed to use the same concepts of stereo vision but overcome some of the problems of passive technologies. Finally, Time-of-Flight (ToF) depth cameras solve the same depth estimation problem by using a different technology. This thesis focuses on the acquisition of depth data from multiple sensors and presents techniques to efficiently combine the information of different acquisition systems. The three main technologies developed to provide depth estimation are first reviewed, presenting operating principles and practical issues of each family of sensors. The use of multiple sensors then is investigated, providing practical solutions to the problem of 3D reconstruction and gesture recognition. Data from stereo vision systems and ToF depth cameras are combined together to provide a higher quality depth map. A confidence measure of depth data from the two systems is used to guide the depth data fusion. The lack of datasets with data from multiple sensors is addressed by proposing a system for the collection of data and ground truth depth, and a tool to generate synthetic data from standard cameras and ToF depth cameras. For gesture recognition, a depth camera is paired with a Leap Motion device to boost the performance of the recognition task. A set of features from the two devices is used in a classification framework based on Support Vector Machines and Random Forests