Faster data structures and graphics hardware techniques for high performance rendering

Computer generated imagery is used in a wide range of disciplines, each with different requirements. As an example, real-time applications such as computer games have completely different restrictions and demands than offline rendering of feature films. A game has to render quickly using only limited resources, yet present visually adequate images. Film and visual effects rendering may not have strict time requirements but are still required to render efficiently utilizing huge render systems with hundreds or even thousands of CPU cores. In real-time rendering, with limited time and hardware resources, it is always important to produce as high rendering quality as possible given the constraints available. The first paper in this thesis presents an analytical hardware model together with a feed-back system that guarantees the highest level of image quality subject to a limited time budget. As graphics processing units grow more powerful, power consumption becomes a critical issue. Smaller handheld devices have only a limited source of energy, their battery, and both small devices and high-end hardware are required to minimize energy consumption not to overheat. The second paper presents experiments and analysis which consider power usage across a range of real-time rendering algorithms and shadow algorithms executed on high-end, integrated and handheld hardware. Computing accurate reflections and refractions effects has long been considered available only in offline rendering where time isn’t a constraint. The third paper presents a hybrid approach, utilizing the speed of real-time rendering algorithms and hardware with the quality of offline methods to render high quality reflections and refractions in real-time. The fourth and fifth paper present improvements in construction time and quality of Bounding Volume Hierarchies (BVH). Building BVHs faster reduces rendering time in offline rendering and brings ray tracing a step closer towards a feasible real-time approach. Bonsai, presented in the fourth paper, constructs BVHs on CPUs faster than contemporary competing algorithms and produces BVHs of a very high quality. Following Bonsai, the fifth paper presents an algorithm that refines BVH construction by allowing triangles to be split. Although splitting triangles increases construction time, it generally allows for higher quality BVHs. The fifth paper introduces a triangle splitting BVH construction approach that builds BVHs with quality on a par with an earlier high quality splitting algorithm. However, the method presented in paper five is several times faster in construction time

3D Dynamic Scene Reconstruction from Multi-View Image Sequences

A confirmation report outlining my PhD research plan is presented. The PhD research topic is 3D dynamic scene reconstruction from multiple view image sequences. Chapter 1 describes the motivation and research aims. An overview of the progress in the past year is included. Chapter 2 is a review of volumetric scene reconstruction techniques and Chapter 3 is an in-depth description of my proposed reconstruction method. The theory behind the proposed volumetric scene reconstruction method is also presented, including topics in projective geometry, camera calibration and energy minimization. Chapter 4 presents the research plan and outlines the future work planned for the next two years

On realistic target coverage by autonomous drones

Low-cost mini-drones with advanced sensing and maneuverability enable a new class of intelligent sensing systems. To achieve the full potential of such drones, it is necessary to develop new enhanced formulations of both common and emerging sensing scenarios. Namely, several fundamental challenges in visual sensing are yet to be solved including (1) fitting sizable targets in camera frames; (2) positioning cameras at effective viewpoints matching target poses; and (3) accounting for occlusion by elements in the environment, including other targets. In this article, we introduce Argus, an autonomous system that utilizes drones to collect target information incrementally through a two-tier architecture. To tackle the stated challenges, Argus employs a novel geometric model that captures both target shapes and coverage constraints. Recognizing drones as the scarcest resource, Argus aims to minimize the number of drones required to cover a set of targets. We prove this problem is NP-hard, and even hard to approximate, before deriving a best-possible approximation algorithm along with a competitive sampling heuristic which runs up to 100× faster according to large-scale simulations. To test Argus in action, we demonstrate and analyze its performance on a prototype implementation. Finally, we present a number of extensions to accommodate more application requirements and highlight some open problems

Conservative Visibility Preprocessing Using Extended Projections

International audienceVisualisation of very complex environments can be significantly accelerated using occlusion culling. In this paper we present a visibility preprocessing method which efficiently computes potentially visible geometry for volumetric viewing cells. We introduce novel extended projection operators, which permits efficient occlusion culling with respect to all viewpoints within a cell, and takes into account the combined occlusion effect of multiple occluders. We use extended projection of occluders onto a set of projection planes to create extended occlusion maps; we show how to efficiently test occludees against these occlusion maps to determine occlusion with respect to the entire cell. We also present an improved projection operator for certain specific but important configurations. An important advantage of our approach is that we can re-project extended projections onto a series of projection planes (via an occlusion sweep), and thus accumulate occlusion information from multiple blockers. This new approach allows the creation of effective occlusion maps for previously hard-to-treat scenes such as leaves of trees in a forest. Graphics hardware is used to accelerate both the extended projection and reprojection operations. We present a complete implementation of our preprocessing algorithm demonstrating significant speedup with respect to view-frustum culling only, without the computational overhead of on-line occlusion culling

The characterisation and simulation of 3D vision sensors for measurement optimisation

The use of 3D Vision is becoming increasingly common in a range of industrial applications including part identification, reverse engineering, quality control and inspection. To facilitate this increased usage, especially in autonomous applications such as free-form assembly and robotic metrology, the capability to deploy a sensor to the optimum pose for a measurement task is essential to reduce cycle times and increase measurement quality. Doing so requires knowledge of the 3D sensor capabilities on a material specific basis, as the optical properties of a surface, object shape, pose and even the measurement itself have severe implications for the data quality. This need is not reflected in the current state of sensor haracterisation standards which commonly utilise optically compliant artefacts and therefore can not inform the user of a 3D sensor the realistic expected performance on non-ideal objects.This thesis presents a method of scoring candidate viewpoints for their ability to perform geometric measurements on an object of arbitrary surface finish. This is achieved by first defining a technology independent, empirical sensor characterisation method which implements a novel variant of the commonly used point density point cloud quality metric, which is normalised to isolate the effect of surface finish on sensor performance, as well as the more conventional assessment of point standard deviation. The characterisation method generates a set of performance maps for a sensor per material which are a function of distance and surface orientation. A sensor simulation incorporates these performance maps to estimate the statistical properties of a point cloud on objects with arbitrary shape and surface finish, providing the sensor has been characterised on the material in question.A framework for scoring measurement specific candidate viewpoints is presented in the context of the geometric inspection of four artefacts with different surface finish but identical geometry. Views are scored on their ability to perform each measurement based on a novel view score metric, which incorporates the expected point density, noise and occlusion of measurement dependent model features. The simulation is able to score the views reliably on all four surface finishes tested, which range from ideal matt white to highly polished aluminium. In 93% of measurements, a set of optimal or nearly optimal views is correctly selected.</div

Viewpoint-Free Photography for Virtual Reality

Viewpoint-free photography, i.e., interactively controlling the viewpoint of a photograph after capture, is a standing challenge. In this thesis, we investigate algorithms to enable viewpoint-free photography for virtual reality (VR) from casual capture, i.e., from footage easily captured with consumer cameras. We build on an extensive body of work in image-based rendering (IBR). Given images of an object or scene, IBR methods aim to predict the appearance of an image taken from a novel perspective. Most IBR methods focus on full or near-interpolation, where the output viewpoints either lie directly between captured images, or nearby. These methods are not suitable for VR, where the user has significant range of motion and can look in all directions. Thus, it is essential to create viewpoint-free photos with a wide field-of-view and sufficient positional freedom to cover the range of motion a user might experience in VR. We focus on two VR experiences: 1) Seated VR experiences, where the user can lean in different directions. This simplifies the problem, as the scene is only observed from a small range of viewpoints. Thus, we focus on easy capture, showing how to turn panorama-style capture into 3D photos, a simple representation for viewpoint-free photos, and also how to speed up processing so users can see the final result on-site. 2) Room-scale VR experiences, where the user can explore vastly different perspectives. This is challenging: More input footage is needed, maintaining real-time display rates becomes difficult, view-dependent appearance and object backsides need to be modelled, all while preventing noticeable mistakes. We address these challenges by: (1) creating refined geometry for each input photograph, (2) using a fast tiled rendering algorithm to achieve real-time display rates, and (3) using a convolutional neural network to hide visual mistakes during compositing. Overall, we provide evidence that viewpoint-free photography is feasible from casual capture. We thoroughly compare with the state-of-the-art, showing that our methods achieve both a numerical improvement and a clear increase in visual quality for both seated and room-scale VR experiences