Construction and Evaluation of an Ultra Low Latency Frameless Renderer for VR.

© 2016 IEEE.Latency-the delay between a users action and the response to this action-is known to be detrimental to virtual reality. Latency is typically considered to be a discrete value characterising a delay, constant in time and space-but this characterisation is incomplete. Latency changes across the display during scan-out, and how it does so is dependent on the rendering approach used. In this study, we present an ultra-low latency real-time ray-casting renderer for virtual reality, implemented on an FPGA. Our renderer has a latency of 1 ms from tracker to pixel. Its frameless nature means that the region of the display with the lowest latency immediately follows the scan-beam. This is in contrast to frame-based systems such as those using typical GPUs, for which the latency increases as scan-out proceeds. Using a series of high and low speed videos of our system in use, we confirm its latency of 1 ms. We examine how the renderer performs when driving a traditional sequential scan-out display on a readily available HMO, the Oculus Rift OK2. We contrast this with an equivalent apparatus built using a GPU. Using captured human head motion and a set of image quality measures, we assess the ability of these systems to faithfully recreate the stimuli of an ideal virtual reality system-one with a zero latency tracker, renderer and display running at 1 kHz. Finally, we examine the results of these quality measures, and how each rendering approach is affected by velocity of movement and display persistence. We find that our system, with a lower average latency, can more faithfully draw what the ideal virtual reality system would. Further, we find that with low display persistence, the sensitivity to velocity of both systems is lowered, but that it is much lower for ours

Image based human body rendering via regression & MRF energy minimization

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A machine learning method for synthesising human images is explored to create new images without relying on 3D modelling. Machine learning allows the creation of new images through prediction from existing data based on the use of training images. In the present study, image synthesis is performed at two levels: contour and pixel. A class of learning-based methods is formulated to create object contours from the training image for the synthetic image that allow pixel synthesis within the contours in the second level. The methods rely on applying robust object descriptions, dynamic learning models after appropriate motion segmentation, and machine learning-based frameworks. Image-based human image synthesis using machine learning is a research focus that has recently gained considerable attention in the field of computer graphics. It makes use of techniques from image/motion analysis in computer vision. The problem lies in the estimation of methods for image-based object configuration (i.e. segmentation, contour outline). Using the results of these analysis methods as bases, the research adopts the machine learning approach, in which human images are synthesised by executing the synthesis of contour and pixels through the learning from training image. Firstly, thesis shows how an accurate silhouette is distilled using developed background subtraction for accuracy and efficiency. The traditional vector machine approach is used to avoid ambiguities within the regression process. Images can be represented as a class of accurate and efficient vectors for single images as well as sequences. Secondly, the framework is explored using a unique view of machine learning methods, i.e., support vector regression (SVR), to obtain the convergence result of vectors for contour allocation. The changing relationship between the synthetic image and the training image is expressed as a vector and represented in functions. Finally, a pixel synthesis is performed based on belief propagation. This thesis proposes a novel image-based rendering method for colour image synthesis using SVR and belief propagation for generalisation to enable the prediction of contour and colour information from input colour images. The methods rely on using appropriately defined and robust input colour images, optimising the input contour images within a sparse SVR framework. Firstly, the thesis shows how contour can effectively and efficiently be predicted from small numbers of input contour images. In addition, the thesis exploits the sparse properties of SVR efficiency, and makes use of SVR to estimate regression function. The image-based rendering method employed in this study enables contour synthesis for the prediction of small numbers of input source images. This procedure avoids the use of complex models and geometry information. Secondly, the method used for human body contour colouring is extended to define eight differently connected pixels, and construct a link distance field via the belief propagation method. The link distance, which acts as the message in propagation, is transformed by improving the low-envelope method in fast distance transform. Finally, the methodology is tested by considering human facial and human body clothing information. The accuracy of the test results for the human body model confirms the efficiency of the proposed method

Image based human body rendering via regression & MRF energy minimization

A machine learning method for synthesising human images is explored to create new images without relying on 3D modelling. Machine learning allows the creation of new images through prediction from existing data based on the use of training images. In the present study, image synthesis is performed at two levels: contour and pixel. A class of learning-based methods is formulated to create object contours from the training image for the synthetic image that allow pixel synthesis within the contours in the second level. The methods rely on applying robust object descriptions, dynamic learning models after appropriate motion segmentation, and machine learning-based frameworks. Image-based human image synthesis using machine learning is a research focus that has recently gained considerable attention in the field of computer graphics. It makes use of techniques from image/motion analysis in computer vision. The problem lies in the estimation of methods for image-based object configuration (i.e. segmentation, contour outline). Using the results of these analysis methods as bases, the research adopts the machine learning approach, in which human images are synthesised by executing the synthesis of contour and pixels through the learning from training image. Firstly, thesis shows how an accurate silhouette is distilled using developed background subtraction for accuracy and efficiency. The traditional vector machine approach is used to avoid ambiguities within the regression process. Images can be represented as a class of accurate and efficient vectors for single images as well as sequences. Secondly, the framework is explored using a unique view of machine learning methods, i.e., support vector regression (SVR), to obtain the convergence result of vectors for contour allocation. The changing relationship between the synthetic image and the training image is expressed as a vector and represented in functions. Finally, a pixel synthesis is performed based on belief propagation. This thesis proposes a novel image-based rendering method for colour image synthesis using SVR and belief propagation for generalisation to enable the prediction of contour and colour information from input colour images. The methods rely on using appropriately defined and robust input colour images, optimising the input contour images within a sparse SVR framework. Firstly, the thesis shows how contour can effectively and efficiently be predicted from small numbers of input contour images. In addition, the thesis exploits the sparse properties of SVR efficiency, and makes use of SVR to estimate regression function. The image-based rendering method employed in this study enables contour synthesis for the prediction of small numbers of input source images. This procedure avoids the use of complex models and geometry information. Secondly, the method used for human body contour colouring is extended to define eight differently connected pixels, and construct a link distance field via the belief propagation method. The link distance, which acts as the message in propagation, is transformed by improving the low-envelope method in fast distance transform. Finally, the methodology is tested by considering human facial and human body clothing information. The accuracy of the test results for the human body model confirms the efficiency of the proposed method.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Low Latency Rendering with Dataflow Architectures

The research presented in this thesis concerns latency in VR and synthetic environments. Latency is the end-to-end delay experienced by the user of an interactive computer system, between their physical actions and the perceived response to these actions. Latency is a product of the various processing, transport and buffering delays present in any current computer system. For many computer mediated applications, latency can be distracting, but it is not critical to the utility of the application. Synthetic environments on the other hand attempt to facilitate direct interaction with a digitised world. Direct interaction here implies the formation of a sensorimotor loop between the user and the digitised world - that is, the user makes predictions about how their actions affect the world, and see these predictions realised. By facilitating the formation of the this loop, the synthetic environment allows users to directly sense the digitised world, rather than the interface, and induce perceptions, such as that of the digital world existing as a distinct physical place. This has many applications for knowledge transfer and efficient interaction through the use of enhanced communication cues. The complication is, the formation of the sensorimotor loop that underpins this is highly dependent on the fidelity of the virtual stimuli, including latency. The main research questions we ask are how can the characteristics of dataflow computing be leveraged to improve the temporal fidelity of the visual stimuli, and what implications does this have on other aspects of the fidelity. Secondarily, we ask what effects latency itself has on user interaction. We test the effects of latency on physical interaction at levels previously hypothesized but unexplored. We also test for a previously unconsidered effect of latency on higher level cognitive functions. To do this, we create prototype image generators for interactive systems and virtual reality, using dataflow computing platforms. We integrate these into real interactive systems to gain practical experience of how the real perceptible benefits of alternative rendering approaches, but also what implications are when they are subject to the constraints of real systems. We quantify the differences of our systems compared with traditional systems using latency and objective image fidelity measures. We use our novel systems to perform user studies into the effects of latency. Our high performance apparatuses allow experimentation at latencies lower than previously tested in comparable studies. The low latency apparatuses are designed to minimise what is currently the largest delay in traditional rendering pipelines and we find that the approach is successful in this respect. Our 3D low latency apparatus achieves lower latencies and higher fidelities than traditional systems. The conditions under which it can do this are highly constrained however. We do not foresee dataflow computing shouldering the bulk of the rendering workload in the future but rather facilitating the augmentation of the traditional pipeline with a very high speed local loop. This may be an image distortion stage or otherwise. Our latency experiments revealed that many predictions about the effects of low latency should be re-evaluated and experimenting in this range requires great care

Image Based Rendering With Stable Frame Rates

Abstract This paper presents an efficient keyframeless image-based rendering technique. An intermediate image is used to exploit the coherences among neighboring frames. The pixels in the intermediate image are first rendered by a ray-casting method and then warped to the intermediate image at the current viewpoint and view direction. We use an offset buffer to record the precise positions of these pixels in the intermediate image. Every frame is generated in three steps: warping the intermediate image onto the frame, filling in holes, and selectively rendering a group of "old" pixels. By dynamically adjusting the number of those "old" pixels in the last step, the workload at every frame can be balanced. The pixels generated by the last two steps makecontributions to the new intermediate image. Unlike occasional keyframes in conventional image-based rendering which need to be totally rerendered, intermediate images only need to be partially updated at every frame. In this way, we guarantee more stable frame rates and more uniform image qualities. The intermediate image can be warped efficiently by a modified incremental 3D warp algorithm. As a specific application, we demonstrate our technique with a voxel-based terrain rendering system