Energy-Efficient Interactive Ray Tracing of Static Scenes on Programmable Mobile GPUs

Mobile technology is improving in quality and capability faster now than ever before. When first introduced, cell phones were strictly used to make voice calls; now, they play satellite radio, MP3s, streaming television, have GPS and navigation capabilities, and have multi-megapixel video cameras. In the near future, cell phones will have programmable graphics processing units (GPU) that will allow users to play games similar to those currently available for top-of-the-line game consoles. Personal digital assistants enable users with full email, scheduling, and internet browsing capabilities in addition to those features offered on cell phones. Underlying all this mobile technology and entertainment is a battery whose technology has just barely tripled in the past 15 years, compared to available disk capacity that has increased over 1,000-fold. Ray tracing is a rendering technique used to generate photorealistic images that include reflections, refraction, participating media, and can fairly easily be extended to include photon mapping for indirect illumination and caustics. In recent years, ray tracing has been implemented on the GPU using various acceleration structures to facilitate rendering. Until now, all studies have used build time and achievable frame rates to determine which acceleration structure is best for ray tracing. We present the very first results comparing both CPU and GPU raytracing using various acceleration structures in terms of energy consumption. By exploring per-pixel costs, we provide insight on the energy consumption and frame rates that can be experienced on cell phones and other mobile devices based on currently available screen resolutions. Our results show that the choice in processing unit has the greatest affect on energy and time costs of ray tracing, followed by the size of the viewport used, and the choice of acceleration structure has the least impact on efficiency. For mobile devices enabled with a programmable GPU, whether it is a cell phone, PDA, or laptop computer, a bounding volume hierarchy implemented on the GPU is the most energy-efficient acceleration structure for ray tracing. Ray tracing on cellular phones with smaller screen resolutions is most energy-efficient using a CPU-based Kd-Tree implementation

Mobile ray-tracing

Dissertação de mestrado em Computer ScienceThe technological advances and the massification of information technologies have allowed a huge and positive proliferation of the number of libraries and APIs. This large offer has made life easier for programmers in general, because they easily find a library, free or commercial, that helps them solve the daily challenges they have at hand. One area of information technology where libraries are critical is in Computer Graphics, due to the wide range of rendering techniques it offers. One of these techniques is ray tracing. Ray tracing allows to simulate natural electromagnetic phenomena such as the path of light and mechanical phenomena such as the propagation of sound. Similarly, it also allows to simulate technologies developed by men, like Wi-Fi networks. These simulations can have a spectacular realism and accuracy, at the expense of a very high computational cost. The constant evolution of technology allowed to leverage and massify new areas, such as mobile devices. Devices today are increasingly faster, replacing and often complementing tasks that were previously performed only on computers or on dedicated hardware. However, the number of image rendering libraries available for mobile devices is still very scarce, and no ray tracing based image rendering library has been able to assert itself on these devices. This dissertation aims to explore the possibilities and limitations of using mobile devices to execute rendering algorithms that use ray tracing, such as progressive path tracing. Its main goal is to provide a rendering library for mobile devices based on ray tracing.Os avanços tecnológicos e a massificação das tecnologias de informação permitiu uma enorme e positiva proliferação do número de bibliotecas e APIs. Esta maior oferta permitiu facilitar a vida dos programadores em geral, porque facilmente encontram uma biblioteca, gratuita ou comercial, que os ajudam a resolver os desafios diários que têm em mãos. Uma área das tecnologias de informação onde as bibliotecas são fundamentais é na Computação Gráfica, devido à panóplia de métodos de renderização que oferece. Um destes métodos é o ray tracing. O ray tracing permite simular fenómenos eletromagnéticos naturais como os percursos da luz e fenómenos mecânicos como a propagação do som. Da mesma forma também permite simular tecnologias desenvolvidas pelo homem, como por exemplo redes Wi-Fi. Estas simulações podem ter um realismo e precisão impressionantes, porém têm um custo computacional muito elevado. A constante evolução da tecnologia permitiu alavancar e massificar novas áreas, como os dispositivos móveis. Os dispositivos são hoje cada vez mais rápidos e cada vez mais substituem e/ou complementam tarefas que anteriormente eram apenas realizadas em computadores ou em hardware dedicado. Porém, o número de bibliotecas para renderização de imagens disponíveis para dispositivos móveis é ainda muito reduzido e nenhuma biblioteca de renderização de imagens baseada em ray tracing conseguiu afirmar-se nestes dispositivos. Esta dissertação tem como objetivo explorar possibilidades e limitações da utilização de dispositivos móveis para a execução de algoritmos de renderização que utilizem ray tracing, como por exemplo, o path tracing progressivo. O objetivo principal é disponibilizar uma biblioteca de renderização para dispositivos móveis baseada em ray tracing

3D Scene Annotation for Efficient Rendering on Mobile Devices

This paper presents a new approach for efficient 3D rendering on mobile devices, where selective rendering can be achieved with the help of 3D scene annotation. By taking advantage of first person environments in most 3D applications, we are able to annotate the flooring details of the 3D space. This allows 3D environments to be interfaced using a higher level view of objects. With the higher level of scene understanding, it is possible to determine which 3D objects are not required for loading or rendering based on the viewer’s location and its surrounding constraints

Mobile graphics: SIGGRAPH Asia 2017 course

Peer ReviewedPostprint (published version

Rmagine: 3D Range Sensor Simulation in Polygonal Maps via Raytracing for Embedded Hardware on Mobile Robots

Sensor simulation has emerged as a promising and powerful technique to find solutions to many real-world robotic tasks like localization and pose tracking.However, commonly used simulators have high hardware requirements and are therefore used mostly on high-end computers. In this paper, we present an approach to simulate range sensors directly on embedded hardware of mobile robots that use triangle meshes as environment map. This library called Rmagine allows a robot to simulate sensor data for arbitrary range sensors directly on board via raytracing. Since robots typically only have limited computational resources, the Rmagine aims at being flexible and lightweight, while scaling well even to large environment maps. It runs on several platforms like Laptops or embedded computing boards like Nvidia Jetson by putting an unified API over the specific proprietary libraries provided by the hardware manufacturers. This work is designed to support the future development of robotic applications depending on simulation of range data that could previously not be computed in reasonable time on mobile systems

Viability of Numerical Full-Wave Techniques in Telecommunication Channel Modelling

In telecommunication channel modelling the wavelength is small compared to the physical features of interest, therefore deterministic ray tracing techniques provide solutions that are more efficient, faster and still within time constraints than current numerical full-wave techniques. Solving fundamental Maxwell's equations is at the core of computational electrodynamics and best suited for modelling electrical field interactions with physical objects where characteristic dimensions of a computing domain is on the order of a few wavelengths in size. However, extreme communication speeds, wireless access points closer to the user and smaller pico and femto cells will require increased accuracy in predicting and planning wireless signals, testing the accuracy limits of the ray tracing methods. The increased computing capabilities and the demand for better characterization of communication channels that span smaller geographical areas make numerical full-wave techniques attractive alternative even for larger problems. The paper surveys ways of overcoming excessive time requirements of numerical full-wave techniques while providing acceptable channel modelling accuracy for the smallest radio cells and possibly wider. We identify several research paths that could lead to improved channel modelling, including numerical algorithm adaptations for large-scale problems, alternative finite-difference approaches, such as meshless methods, and dedicated parallel hardware, possibly as a realization of a dataflow machine

Energy-Efficient Photon Mapping

Mobile devices such as cell phones, personal digital assistants (PDAs), and laptops continue to increase in memory and processor speed at a rapid pace. In recent years it has become common for users to check their email, browse the internet, or play music and movies while traveling. The performance gains are also making mobile graphics renderers more viable applications. However, the underlying battery technology that powers mobile devices has only tripled in capacity in the past 15 years whereas processor speeds have seen a 100-fold increase in the same period. Photon mapping, an extension of ray-tracing, is a robust global illumination algorithm used to produce photorealistic images. Photon mapping, like ray-tracing, can render high-quality specular highlights, transparent and reflective materials, and soft shadows. Complex effects such as caustics, participating media, and subsurface scattering can be rendered more efficiently using photon mapping. This work profiles the energy use of a photon-mapping based renderer to first establish what aspects require the most energy. Second, the effect several photon mapping settings have on image quality is measured. Reasonable tradeoffs between energy savings and moderately diminished image quality can then be recommended, making photon mapping more viable on mobile devices. Our results show that image quality is affected the least as settings corresponding to final gather computations are adjusted. This implies that a user can trade a modest decrease in image quality for significant gains in energy efficiency. Suggestions are made for using energy more efficiently when rendering caustics. Results also show that, although overall energy use is higher with larger image resolutions, per-pixel energy costs are cheaper