REFERENCE DESIGNS


Veronica

Designed to fit into Virtual Reality Head Mounted Displays (VR HMDs) and Smart Glasses, the Veronica is a complete reference design hosting Inuitive’s NU3000 3D Imaging and Vision processor. The veronica is designed to enable shorter time to production when dealing with manufacturing calibration and alignment procedures.


Twiggy


Optimized for Google Tango based smartphones and tablets. Twiggy is a small footprint reference design module provides INUITIVE’s best of breed combination of depth sensing with vision technologies.


M3.2H

Tiny depth sensor for short-range gesture recognition.

PRODUCTS

NU3000 3D IMAGING & VISION PROCESSOR

A multi-core signal processor chip that supports 3D Image Processing (3D depth) and Computer Vision (CV) processing

  • Multi core CV and depth processor Dedicated depth engine
    40nm geometry
    Connects to 3 cameras
  • Size: 12X10mm
  • Command, control and interface all external elements on the module, including:
    • Two RGB/IR CMOS sensors, aligned in a stereoscopic setup
    • An RGB CMOS sensor, con gured to function as a standard web-cam
    • Multiple illumination interfaces - An external LPDDR memory unit
  • Depth map generation, based on the information generated by RGB or IR sensors
  • 6DoF pose estimation and feature tracking for SLAM (Simultaneous Localization and Mapping)
  • Real time processing capable of synchronizing, time- stamping and processing inputs from multiple sensors to serve as a smart sensor hub.
  • Main interfaces
    • Host - MIPI CSI-2 Master TX, 2 lanes or USB2/3 - Memory - High performance LPDDR2
    • External - 3xCon gurable Bidirectional MIPI CSI-2: Master TX or Slave RX, 2 lanes for sensor-connections, 3xUART, 4xI2C, SPI for ash/other, I2S, GPIO and timers

POWER EFFICIENT

The unique multi-core architecture implemented in the NU3000, together with the dedicated hardware accelerated depth- from-stereo core, make the NU3000 the most
power-e cient solution for depth sensing.

3D IMAGE PROCESSING

Single module covering all real-time requirements of mobile, VR and AR systems: depth generation, mapping and localization, nger tracking and
gaze tracking.


NU3000_High Level Block Diagram  

NU4000 NEXT GENERATION 3D IMAGING AND VISION PROCESSING WITH DEEP LEARNING

A multi-core signal processor that supports 3D Image (3D depth) and Computer Vision (CV) processing.Higher quality depth, SLAM accelerator, stronger Computer Vision engine and Deep Learning (CNN) processor

  • Multi core CV, depth and deep learning processor
  • Dedicated Depth engine
  • Computer Vision accelerator
  • High throughput, low latency SLAM processing, powered by dedicated hardware accelerators
  • Fully programmable Deep Learning processor 28nm geometry
  • Connects to 6 cameras
  • Size: 7X8mm
  • Depth map generation, based on the information generated by the two RGB or IR sensors
  • Command, control and interface all external elements on the module, including:
    • Two RGB/IR CMOS sensors, aligned in a stereoscopic setup
    • Additional 4 cameras for SLAM, Gaze Tracking, color camera and more
    • Multiple illumination interfaces
    • An external LPDDR memory unit
  • 6DoF pose estimation and feature tracking for SLAM (Simultaneous Localization and Mapping)
  • Enhanced Depth resolution of 1/16 pixel
  • Real time processing capable of synchronizing, time- stamping and processing inputs from multiple sensors to serve as a smart sensor hub
  • Main interfaces
    • Host - MIPI CSI-2 Master TX, 2 lanes or USB2/3
    • Memory - High performance LPDDR2/3
    • External - 6xMIPI CSI-2 RX interfaces, with two lanes each for image acquisition and sensor control, 3xMIPI CSI-2 Master TX, two lanes each for Output Video, 3xUART, 6xI2C, 2xSPI for ash/other, I2S, GPIO and timers

HIGH PERFORMANCE

Powerful core-processors, backed up by hardware accelerators for depth, SLAM and Computer Vision processing reduce latency down to 1mSec, leading to an enhanced VR/AR experience without nausea.

DEEP LEARNING ENGINE

Convolutional Neural Network Engine for Deep Learning enables object detection, classi cation and localization, scene recognition and more.