Artigo técnico

面向增强现实的自主导航介绍

This article provides an introduction to autonomous navigation and its use in augmented reality applications, with a focus on agents that move and navigate. Autonomous agents are entities that act independently using artificial intelligence, which defines the operational parameters and rules by which the agent must abide. The agent responds dynamically in real time to its environment, so even a simple design can result in complex behavior. An example is developed that uses the Intel RealSense camera R200 and the Unity* 3D Game Engine.
  • Microsoft Windows* 10
  • Microsoft Windows* 8.x
  • Desenvolvimento de jogos
  • Tecnologia Intel® RealSense™
  • Windows*
  • C/C++
  • Unidade
  • Intermediário
  • SDK de Intel® RealSense™
  • Tecnologia Intel® RealSense™
  • GameCodeSample
  • depth camera
  • autonomous navigation
  • 用于渲染虚拟现实的投影方法的比较

    Virtual reality is rapidly gaining popularity, and may soon become a common way of viewing 3D environments. While stereo rendering has been performed on consumer grade graphics processors for a while now, the new wave of virtual reality display devices have two properties that typical applications have not needed to consider before. Pixels no longer appear on regular grids and the displays subtend a wide field-of-view. In this paper, we evaluate several techniques designed to efficiently render for head-mounted displays with such properties. We show that the amount of rendered pixels can be reduced down to 36% without compromising visual fidelity compared to traditional rendering, by rendering multiple optimized sub-projections.
  • Professores
  • Estudantes
  • Desenvolvimento de jogos
  • Gráficos
  • Renderização
  • Intel Innovator’s Summit 2016, Part 1

    I am participating in the Intel Innovator’s Summit come November 14th in Seattle. One of the hacking projects will be with the Intel Joule kit, a toy car holding our favorite animated characters along with a Real-sense camera. The summit promises to be a lot of fun.

    First and foremost: The team has not decided upon which animated character to use, but I voted for Ren and Stimpy. This decision probably shows both my decrepitude as well as my twisted sense of humor.

    Integration Wrappers for Intel® Integrated Performance Primitives (Intel® IPP)

    To provide easy-to-use APIs and reduce the effort required to add Intel® Integrated Performance Primitives (Intel® IPP) functions to your application, Intel® IPP library introduces new Integration Wrappers APIs. These APIs aggregate multiple Intel® IPP functions and provide easy interfaces to support external threading of Intel® IPP functions. A technical preview of Integration Wrappers functionality is now available for evaluation.

    Integration Wrappers consist of C and C++ interfaces:

  • Apple macOS*
  • Linux*
  • Microsoft Windows* 10
  • Microsoft Windows* 8.x
  • C/C++
  • Primitivas Intel® Integrated Performance
  • OVS-DPDK Datapath Classifier

    Describes the design and implementation of the datapath classifier – aka dpcls – in Open vSwitch* (OVS) with Data Plane Development Kit (DPDK). It presents flow classification and the caching techniques, and also provides greater insight on functional details with different scenarios.
  • Linux*
  • Rede
  • Virtualização das funções de rede (NFV)
  • Redes definidas por software (SDN)
  • Pipeline and the Efficient Chef (Part 2)

    Advanced computer concepts for the (not so) common Chef

    In Pipeline and the Efficient Chef (Part 1), we showed how the basic pipeline is equivalent to what our Chef does when following one step in his recipe. To say it differently, the execution of one machine language instruction is equivalent to our Chef performing one step of a complicated recipe.

    使用英特尔® Inspector XE 2011 发现多线程代码中的数据竞跑

    Intel Inspector XE automatically finds memory errors, deadlocks and other conditions that could lead to deadlocks, data races, thread . Some specific issues associated with debugging multithreaded applications will be discussed in this article.
  • Linux*
  • Microsoft Windows* (XP, Vista, 7)
  • Principiante
  • Intel® Parallel Studio XE
  • Intel® Parallel Studio XE Composer Edition
  • Intel® Inspector
  • Intel® Parallel Inspector
  • critical section
  • data races
  • Learning Lab
  • OpenMP*
  • Computação paralela
  • Thread
  • 优化数据结构和内存访问模式以改进数据局部性

    优化数据结构和内存访问模式以改进数据局部性 (PDF 782KB)

    摘要

    高速缓存是最重要的现代 CPU 资源之一:它是体积更小、速度更快的一部分内存子系统,用于保存最常用的内存位置副本。 当驻留在高速缓存中的指令需要数据时,该指令将会立即执行。 否则,指令执行过程可能会中止,直到从内存获取到所需的数据。 由于从内存中拷贝数据是一项延迟较长的操作,因此我们希望通过对算法和数据结构进行设计,以充分利用数据局部性,从而最大限度降低缓存缺失。

    本文将介绍数据局部性较差的表现、检测相关性能瓶颈的技巧,以及可解决该问题的优化方法。

  • Servidor
  • Computação paralela
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