Portal-s: High-resolution real-time 3D video telepresence

The goal of telepresence is to allow a person to feel as if they are present in a location other than their true location; a common application of telepresence is video conferencing in which live video of a user is transmitted to a remote location for viewing. In conventional two-dimensional (2D) video conferencing, loss of correct eye gaze commonly occurs, due to a disparity between the capture and display optical axes. Newer systems are being developed which allow for three-dimensional (3D) video conferencing, circumventing issues with this disparity, but new challenges are arising in the capture, delivery, and redisplay of 3D contents across existing infrastructure. To address these challenges, a novel system is proposed which allows for 3D video conferencing across existing networks while delivering full resolution 3D video and establishing correct eye gaze. During the development of Portal-s, many innovations to the field of 3D scanning and its applications were made; specifically, this dissertation research has achieved the following innovations: a technique to realize 3D video processing entirely on a graphics processing unit (GPU), methods to compress 3D videos on a GPU, and combination of the aforementioned innovations with a special holographic display hardware system to enable the novel 3D telepresence system entitled Portal-s. The first challenge this dissertation addresses is the cost of real-time 3D scanning technology, both from a monetary and computing power perspective. New advancements in 3D scanning and computation technology are continuing to increase, simplifying the acquisition and display of 3D data. These advancements are allowing users new methods of interaction and analysis of the 3D world around them. Although the acquisition of static 3D geometry is becoming easy, the same cannot be said of dynamic geometry, since all aspects of the 3D processing pipeline, capture, processing, and display, must be realized in real-time simultaneously. Conventional approaches to solve these problems utilize workstation computers with powerful central processing units (CPUs) and GPUs to accomplish the large amounts of processing power required for a single 3D frame. A challenge arises when trying to realize real-time 3D scanning on commodity hardware such as a laptop computer. To address the cost of a real-time 3D scanning system, an entirely parallel 3D data processing pipeline that makes use of a multi-frequency phase-shifting technique is presented. This novel processing pipeline can achieve simultaneous 3D data capturing, processing, and display at 30 frames per second (fps) on a laptop computer. By implementing the pipeline within the OpenGL Shading Language (GLSL), nearly any modern computer with a dedicated graphics device can run the pipeline. Making use of multiple threads sharing GPU resources and direct memory access transfers, high frame rates on low compute power devices can be achieved. Although these advancements allow for low compute power devices such as a laptop to achieve real-time 3D scanning, this technique is not without challenges. The main challenge being selecting frequencies that allow for high quality phase, yet do not include phase jumps in equivalent frequencies. To address this issue, a new modified multi-frequency phase shifting technique was developed that allows phase jumps to be introduced in equivalent frequencies yet unwrapped in parallel, increasing phase quality and reducing reconstruction error. Utilizing these techniques, a real-time 3D scanner was developed that captures 3D geometry at 30 fps with a root mean square error (RMSE) of 0:00081 mm for a measurement area of 100 mm X 75 mm at a resolution of 800 X 600 on a laptop computer. With the above mentioned pipeline the CPU is nearly idle, freeing it to perform additional tasks such as image processing and analysis. The second challenge this dissertation addresses is associated with delivering huge amounts of 3D video data in real-time across existing network infrastructure. As the speed of 3D scanning continues to increase, and real-time scanning is achieved on low compute power devices, a way of compressing the massive amounts of 3D data being generated is needed. At a scan resolution of 800 X 600, streaming a 3D point cloud at 30 frames per second (FPS) would require a throughput of over 1.3 Gbps. This amount of throughput is large for a PCIe bus, and too much for most commodity network cards. Conventional approaches involve serializing the data into a compressible state such as a polygon file format (PLY) or Wavefront object (OBJ) file. While this technique works well for structured 3D geometry, such as that created with computer aided drafting (CAD) or 3D modeling software, this does not hold true for 3D scanned data as it is inherently unstructured. A challenge arises when trying to compress this unstructured 3D information in such a way that it can be easily utilized with existing infrastructure. To address the need for real-time 3D video compression, new techniques entitled Holoimage and Holovideo are presented, which have the ability to compress, respectively, 3D geometry and 3D video into 2D counterparts and apply both lossless and lossy encoding. Similar to the aforementioned 3D scanning pipeline, these techniques make use of a completely parallel pipeline for encoding and decoding; this affords high speed processing on the GPU, as well as compression before streaming the data over the PCIe bus. Once in the compressed 2D state, the information can be streamed and saved until the 3D information is needed, at which point 3D geometry can be reconstructed while maintaining a low amount of reconstruction error. Further enhancements of the technique have allowed additional information, such as texture information, to be encoded by reducing the bit rate of the data through image dithering. This allows both the 3D video and associated 2D texture information to be interlaced and compressed into 2D video, synchronizing the streams automatically. The third challenge this dissertation addresses is achieving correct eye gaze in video conferencing. In 2D video conferencing, loss of correct eye gaze commonly occurs, due to a disparity between the capture and display optical axes. Conventional approaches to mitigate this issue involve either reducing the angle of disparity between the axes by increasing the distance of the user to the system, or merging the axes through the use of beam splitters. Newer approaches to this issue make use of 3D capture and display technology, as the angle of disparity can be corrected through transforms of the 3D data. Challenges arise when trying to create such novel systems, as all aspects of the pipeline, capture, transmission, and redisplay must be simultaneously achieved in real-time with the massive amounts of 3D data. Finally, the Portal-s system is presented, which is an integration of all the aforementioned technologies into a holistic software and hardware system that enables real-time 3D video conferencing with correct mutual eye gaze. To overcome the loss of eye contact in conventional video conferencing, Portal-s makes use of dual structured-light scanners that capture through the same optical axis as the display. The real-time 3D video frames generated on the GPU are then compressed using the Holovideo technique. This allows the 3D video to be streamed across a conventional network or the Internet, and redisplayed at a remote node for another user on the Holographic display glass. Utilizing two connected Portal-s nodes, users of the systems can engage in 3D video conferencing with natural eye gaze established. In conclusion, this dissertation research substantially advances the field of real-time 3D scanning and its applications. Contributions of this research span into both academic and industrial practices, where the use of this information has allowed users new methods of interaction and analysis of the 3D world around them

自己投影法に基づく高速三次元形状検査の研究

広島大学(Hiroshima University)博士(工学)Doctor of Engineeringdoctora

Advanced Image Reconstruction for Limited View Cone-Beam CT

In a standard CT acquisition, a high number of projections is obtained around the sample, generally covering an angular span of 360º. However, complexities may arise in some clinical scenarios such as surgery and emergency rooms or Intensive Care Units (ICUs) when the accessibility to the patient is limited due to the monitoring equipment attached. X-ray systems used in these cases are usually C-arms that only enable the acquisition of planar images within a limited angular range. Obtaining 3D images in these scenarios could be extremely interesting for diagnosis or image guided surgery. This would be based on the acquisition of a small number of projections within a limited angular span. Reconstruction of these limited-view data with conventional algorithms such as FDK result in streak artifacts and shape distortion deteriorating the image quality. In order to reduce these artifacts, advanced reconstruction methods can be used to compensate the lack of data by the incorporation of prior information. This bachelor thesis is framed on one of the lines of research carried out by the Biomedical Imaging and Instrumentation group from the Bioengineering and Aerospace Department of Universidad Carlos III de Madrid working jointly with the Hospital General Universitario Gregorio Marañón through its Instituto de Investigación Sanitaria. This line of research is carried out in collaboration with the company SEDECAL, which enables the direct transfer to the industry. Previous work showed that a new iterative reconstruction method proposed by the group, SCoLD, is able to restore the altered contour of the object, suppress greatly the streak artifacts and recover to some extend the image quality by restricting the space of search with a surface constraint. However, the evaluation was only carried out using a simulated mask that described the shape of the object obtained by thresholding a previous CT image of the sample, which is generally not available in real scenarios. The general objective of this thesis is the designing of a complete workflow to implement SCoLD in real scenarios. For that purpose, the 3D scanner Artec Eva was chosen to acquire the surface information of the sample, which was then transformed to be usable as prior information for SCoLD method. The evaluation done in a rodent study showed high similarity between the mask obtained from real data and the ideal mask obtained from a CT. Distortions in shape and streak artifacts in the limited-view FDK reconstruction were greatly reduced when using the real mask with the SCoLD reconstruction and the image quality was highly improved demonstrating the feasibility of the proposal.Grado en Ingeniería Biomédica (Plan 2010

Real-time 3-D Reconstruction by Means of Structured Light Illumination

Structured light illumination (SLI) is the process of projecting a series of light striped patterns such that, when viewed at an angle, a digital camera can reconstruct a 3-D model of a target object\u27s surface. But by relying on a series of time multiplexed patterns, SLI is not typically associated with video applications. For this purpose of acquiring 3-D video, a common SLI technique is to drive the projector/camera pair at very high frame rates such that any object\u27s motion is small over the pattern set. But at these high frame rates, the speed at which the incoming video can be processed becomes an issue. So much so that many video-based SLI systems record camera frames to memory and then apply off-line processing. In order to overcome this processing bottleneck and produce 3-D point clouds in real-time, we present a lookup-table (LUT) based solution that in our experiments, using a 640 by 480 video stream, can generate intermediate phase data at 1063.8 frames per second and full 3-D coordinate point clouds at 228.3 frames per second. These achievements are 25 and 10 times faster than previously reported studies. At the same time, a novel dual-frequency pattern is developed which combines a high-frequency sinusoid component with a unit-frequency sinusoid component, where the high-frequency component is used to generate robust phase information and the unit-frequency component is used to reduce phase unwrapping ambiguities. Finally, we developed a gamma model for SLI, which can correct the non-linear distortion caused by the optical devices. For three-step phase measuring profilometry (PMP), analysis of the root mean squared error of the corrected phase showed a 60х reduction in phase error when the gamma calibration is performed versus 33х reduction without calibration

Depth from Monocular Images using a Semi-Parallel Deep Neural Network (SPDNN) Hybrid Architecture

Deep neural networks are applied to a wide range of problems in recent years. In this work, Convolutional Neural Network (CNN) is applied to the problem of determining the depth from a single camera image (monocular depth). Eight different networks are designed to perform depth estimation, each of them suitable for a feature level. Networks with different pooling sizes determine different feature levels. After designing a set of networks, these models may be combined into a single network topology using graph optimization techniques. This "Semi Parallel Deep Neural Network (SPDNN)" eliminates duplicated common network layers, and can be further optimized by retraining to achieve an improved model compared to the individual topologies. In this study, four SPDNN models are trained and have been evaluated at 2 stages on the KITTI dataset. The ground truth images in the first part of the experiment are provided by the benchmark, and for the second part, the ground truth images are the depth map results from applying a state-of-the-art stereo matching method. The results of this evaluation demonstrate that using post-processing techniques to refine the target of the network increases the accuracy of depth estimation on individual mono images. The second evaluation shows that using segmentation data alongside the original data as the input can improve the depth estimation results to a point where performance is comparable with stereo depth estimation. The computational time is also discussed in this study.Comment: 44 pages, 25 figure

Advances in deep learning methods for pavement surface crack detection and identification with visible light visual images

Compared to NDT and health monitoring method for cracks in engineering structures, surface crack detection or identification based on visible light images is non-contact, with the advantages of fast speed, low cost and high precision. Firstly, typical pavement (concrete also) crack public data sets were collected, and the characteristics of sample images as well as the random variable factors, including environmental, noise and interference etc., were summarized. Subsequently, the advantages and disadvantages of three main crack identification methods (i.e., hand-crafted feature engineering, machine learning, deep learning) were compared. Finally, from the aspects of model architecture, testing performance and predicting effectiveness, the development and progress of typical deep learning models, including self-built CNN, transfer learning(TL) and encoder-decoder(ED), which can be easily deployed on embedded platform, were reviewed. The benchmark test shows that: 1) It has been able to realize real-time pixel-level crack identification on embedded platform: the entire crack detection average time cost of an image sample is less than 100ms, either using the ED method (i.e., FPCNet) or the TL method based on InceptionV3. It can be reduced to less than 10ms with TL method based on MobileNet (a lightweight backbone base network). 2) In terms of accuracy, it can reach over 99.8% on CCIC which is easily identified by human eyes. On SDNET2018, some samples of which are difficult to be identified, FPCNet can reach 97.5%, while TL method is close to 96.1%. To the best of our knowledge, this paper for the first time comprehensively summarizes the pavement crack public data sets, and the performance and effectiveness of surface crack detection and identification deep learning methods for embedded platform, are reviewed and evaluated.Comment: 15 pages, 14 figures, 11 table

Heritage Recording and 3D Modeling with Photogrammetry and 3D Scanning

The importance of landscape and heritage recording and documentation with optical remote sensing sensors is well recognized at international level. The continuous development of new sensors, data capture methodologies and multi-resolution 3D representations, contributes significantly to the digital 3D documentation, mapping, conservation and representation of landscapes and heritages and to the growth of research in this field. This article reviews the actual optical 3D measurement sensors and 3D modeling techniques, with their limitations and potentialities, requirements and specifications. Examples of 3D surveying and modeling of heritage sites and objects are also shown throughout the paper

Novel Approaches in Structured Light Illumination

Among the various approaches to 3-D imaging, structured light illumination (SLI) is widely spread. SLI employs a pair of digital projector and digital camera such that the correspondences can be found based upon the projecting and capturing of a group of designed light patterns. As an active sensing method, SLI is known for its robustness and high accuracy. In this dissertation, I study the phase shifting method (PSM), which is one of the most employed strategy in SLI. And, three novel approaches in PSM have been proposed in this dissertation. First, by regarding the design of patterns as placing points in an N-dimensional space, I take the phase measuring profilometry (PMP) as an example and propose the edge-pattern strategy which achieves maximum signal to noise ratio (SNR) for the projected patterns. Second, I develop a novel period information embedded pattern strategy for fast, reliable 3-D data acquisition and reconstruction. The proposed period coded phase shifting strategy removes the depth ambiguity associated with traditional phase shifting patterns without reducing phase accuracy or increasing the number of projected patterns. Thus, it can be employed for high accuracy realtime 3-D system. Then, I propose a hybrid approach for high quality 3-D reconstructions with only a small number of illumination patterns by maximizing the use of correspondence information from the phase, texture, and modulation data derived from multi-view, PMP-based, SLI images, without rigorously synchronizing the cameras and projectors and calibrating the device gammas. Experimental results demonstrate the advantages of the proposed novel strategies for 3-D SLI systems