Open Standard APIs for Augmented Reality

Transcription

1 Copyright Khronos Group Page 1 Open Standard APIs for Augmented Reality Neil Trevett Vice President Mobile Ecosystem, NVIDIA President, Khronos Group

2 Copyright Khronos Group Page 2 Khronos Connects Software to Silicon Open Consortium creating ROYALTY-FREE, OPEN STANDARD APIs for hardware acceleration Defining the roadmap for low-level silicon interfaces needed on every platform Graphics, compute, rich media, vision, sensor and camera processing Rigorous specifications AND conformance tests for crossvendor portability Acceleration APIs BY the Industry FOR the Industry Well over a BILLION people use Khronos APIs Every Day

3 Copyright Khronos Group Page 3 Khronos Standards Visual Computing - 3D Graphics - Heterogeneous Parallel Computing 3D Asset Handling - 3D authoring asset interchange - 3D asset transmission format with compression Camera Control API Sensor Processing - Vision Acceleration - Camera Control - Sensor Fusion Over 100 companies defining royalty-free APIs to connect software to silicon Acceleration in HTML5-3D in browser no Plug-in - Heterogeneous computing for JavaScript

4 Vision Pipeline Challenges and Opportunities Growing Camera Diversity Capturing color, range and lightfields Diverse Vision Processors Driving for high performance and low power Sensor Proliferation Diverse sensor awareness of the user and surroundings Light / Proximity 2 cameras 3 microphones Touch Camera sensors >20MPix Novel sensor configurations Stereo pairs Active Structured Light Active TOF Plenoptic Arrays Camera ISPs Dedicated vision IP blocks DSPs and DSP arrays Programmable GPUs Multi-core CPUs 19 Position - GPS - WiFi (fingerprint) - Cellular trilateration - NFC/Bluetooth Beacons Accelerometer Magnetometer Gyroscope Pressure / Temp / Humidity Flexible sensor and camera control to generate required image stream Camera Control API Use best processing available for image stream processing with code portability Control/fuse vision data by/with all other sensor data on device Copyright Khronos Group Page 4

5 Copyright Khronos Group Page 5 Vision Processing Power Efficiency GPUs are more power efficient than CPUs at vision acceleration - When exploiting data parallelism can be x10 as efficient and getting better! SOCs have space for transistors but can t turn on at same time! - Would exceed Thermal Design Point of mobile devices Dedicated units can further increase locality and parallelism for efficiency - Dark Silicon - specialized hardware only turned on when needed Ultra-low power vision scanners will become essential for wearables - Sensor and visual awareness to trigger full vision acceleration subsystems Enabling new mobile visionbased experiences requires pushing computation onto GPUs and dedicated hardware X100 X10 X1 Power Efficiency Dedicated Hardware GPU Compute Computation Flexibility Multi-core CPU

6 Copyright Khronos Group Page 6 OpenVX Power Efficient Vision Acceleration Out-of-the-Box vision acceleration framework - Low-power, real-time, mobile and embedded Performance portability - ISPs, dedicated vision hardware, DSPs and DSP arrays, GPUs, Multi-core CPUs Suited for low-power, always-on acceleration - Can run solely on dedicated vision hardware Foundational API for vision acceleration - Can be used by middleware or applications Complementary to OpenCV - Which is great for prototyping OpenCV open source library Khronos open source sample implementation - To be released with final specification Open source sample implementation Application Other higher-level CV libraries Hardware vendor implementations

7 Copyright Khronos Group Page 7 OpenVX Graphs The Key to Efficiency Vision processing directed graphs for power and performance efficiency - Each Node can be implemented in software or accelerated hardware - Nodes may be fused by the implementation to eliminate memory transfers - Processing can be tiled to keep data entirely in local memory/cache VXU Utility Library for access to single nodes - Easy way to start using OpenVX by calling each node independently EGLStreams can provide data and event interop with other Khronos APIs - BUT use of other Khronos APIs are not mandated Native Camera Control OpenVX Node OpenVX Node OpenVX Node OpenVX Node Downstream Application Processing Example OpenVX Graph

8 Copyright Khronos Group Page 8 OpenVX and OpenCV are Complementary Governance Conformance Community driven open source with no formal specification No conformance tests for consistency and every vendor implements different subset Formal specification defined and implemented by hardware vendors Full conformance test suite / process creates a reliable acceleration platform Portability APIs can vary depending on processor Hardware abstracted for portability Scope Efficiency Very wide 1000s of imaging and vision functions Multiple camera APIs/interfaces Memory-based architecture Each operation reads and writes memory Tight focus on hardware accelerated functions for mobile vision Use external camera API Graph-based execution Optimizable computation, data transfer Use Case Rapid experimentation Production development & deployment

9 Copyright Khronos Group Page 9 OpenCL as Parallel Language Backend JavaScript binding for initiation of OpenCL C kernels Language for image processing and computational photography MulticoreWare open source project on Bitbucket Embedded array language for Haskell Java language extensions for parallelism River Trail Language extensions to JavaScript Compiler directives for Fortran, C and C++ PyOpenCL Python wrapper around OpenCL Harlan High level language for GPU programming OpenCL provides vendor optimized, cross-platform, cross-vendor access to heterogeneous compute resources

10 Copyright Khronos Group Page 10 OpenVX and OpenCL are Complementary Use Case Ease of Use Architecture Target Hardware Precision General Heterogeneous programming General-purpose math libraries with no built-in vision functions Language-based needs online compilation Exposed architected memory model can impact performance portability Full IEEE floating point mandated Domain targeted Vision processing Fully implemented vision operators and framework out of the box Library-based - no online compiler required Abstracted node and memory model - diverse implementations can be optimized for power and performance Minimal floating point requirements optimized for vision operators It is possible to use OpenCL to build OpenVX Nodes on programmable devices BUT - do we need definition of an efficient, vision-capable OpenCL Device? Precisely defined precision for image and vision operations?

11 Copyright Khronos Group Page 11 Khronos APIs for Vision Processing GPU Compute Shaders (OpenGL 4.X and OpenGL ES 3.1) Pervasively available on almost any mobile device or OS Easy integration into graphics apps no compute API interop needed Program in GLSL not C Limited to acceleration on a single GPU General Purpose Heterogeneous Programming Framework Flexible, low-level access to any devices with OpenCL compiler Single programming and run-time framework for CPUs, GPUs, DSPs, hardware Open standard for any device or OS being used as backed by many languages and frameworks Needs full compiler stack and IEEE precision Out of the Box Vision Framework - Operators and graph framework library Can run on dedicated hardware no compiler needed Easier performance portability to diverse hardware Suited for low-power, always-on acceleration Fixed set of operators but can be extended

12 Copyright Khronos Group Page 12 Need for Camera Control API Advanced control of ISP and camera subsystem with cross-platform portability - Generate sophisticated image stream for advanced imaging & vision apps No platform API currently fulfills all developer requirements - Portable access to growing sensor diversity: e.g. depth sensors and sensor arrays - Cross sensor synch: e.g. synch of camera and MEMS sensors - Advanced, high-frequency per-frame burst control of camera/sensor: e.g. ROI - Multiple input, output re-circulating streams with RAW, Bayer or YUV Processing Khronos Camera Control API Defines control of Sensor, Color Filter Array Lens, Flash, Focus, Aperture Auto Exposure (AE) Auto White Balance (AWB) Auto Focus (AF) Image Signal Processor (ISP) EGLStreams Image/Vision Applications

13 Copyright Khronos Group Page 13 Low-level Sensor Abstraction API Apps request semantic sensor information StreamInput defines possible requests, e.g. Read Physical or Virtual Sensors e.g. Game Quaternion Context detection e.g. Am I in an elevator? Apps Need Sophisticated Access to Sensor Data Without coding to specific sensor hardware Sensor Discoverability Sensor Code Portability Advanced Sensors Everywhere Multi-axis motion/position, quaternions, context-awareness, gestures, activity monitoring, health and environmental sensors StreamInput processing graph provides optimized sensor data stream High-value, smart sensor fusion middleware can connect to apps in a portable way Apps can gain magical situational awareness

14 Copyright Khronos Group Page 14 Khronos APIs for Augmented Reality AR needs not just advanced sensor processing, vision acceleration, computation and rendering - but also for all these subsystems to work efficiently together Audio Rendering MEMS Sensors Sensor Fusion Application on CPUs, GPUs and DSPs Precision timestamps on all sensor samples Vision Processing Advanced Camera Control and stream generation Camera Control API EGLStream - stream data between APIs 3D Rendering and Video Composition On GPU

15 Web Acceleration APIs Khronos and W3C liaison for Web APIs - Leverage proven native APIs - Fast API development/deployment - Designed by hardware community - Familiar foundation reduces developer learning curve HTML Canvas WebVX? Vision Processing WebStream? Sensor Fusion WebKAM? Camera control and video processing JavaScript Path Rendering Camera Control Native Native APIs shipping or Khronos working group JavaScript API shipping, acceleration being developed or work underway Possible future JavaScript APIs or acceleration Copyright Khronos Group Page 15

16 Copyright Khronos Group Page 16 COLLADA and gltf Open Source Ecosystem OpenCOLLADA Importer/Exporter and COLLADA Conformance Tests On GitHUB Tool Interop COLLADA2GLTF Translator Other authoring formats Web-based Tools Pervasive WebGL deployment Three.js gltf Importer. Rest3D initiative

17 Copyright Khronos Group Page 17 Initial Compression Results Compression Efficiency - Gzip (default level=6) - OpenCTM (default settings) - Open3DGC and Webgl-loader - Positions on 14 bits - Normals and texcoords on 10 bits Size (MBytes) Gzip OpenCTM Webgl-loader + Gzip Open3DGC-ASCII + Gzip Open3DGC-Binary 0 CAD (3748 models) 3D Scanned (78 models) MPEG dataset (1211 models) Open3DGC is 5x-9x more efficient than Gzip 1.3x-2.4x more efficient than OpenCTM and 1.2x-1.5x more efficient than webgl-loader

18 Copyright Khronos Group Page 18 Summary Khronos is building a family of interoperating APIs and formats relevant to portable and power-efficient Augmented Reality Any company is welcome to join Khronos to influence the direction of mobile and web-based AR! - $15K annual membership fee for access to all Khronos API working groups - Well-defined IP framework protects your IP and conformant implementations - ntrevett@nvidia.com