LazeeEye: Turn Your Smartphone Into a 3D Camera
LazeeEye: Turn Your Smartphone Into a 3D Camera
LazeeEye upgrades your smartphone for use as a 3D camera, using a laser illuminator hardware add-on and a stereo vision processing app.
LazeeEye upgrades your smartphone for use as a 3D camera, using a laser illuminator hardware add-on and a stereo vision processing app. Read more
About this project
(previous in-depth video: https://www.youtube.com/watch?v=zHnfIe2U7do)
(previous "meet the team" video: http://youtu.be/EpkkR6B2Wxo)
LazeeEye? Seriously? The name "LazeeEye" is a portmanteau of "laser" and "eye," indicating that your phone's camera (a single "eye") is being augmented with a second, "laser eye" - thus bestowing depth perception via stereo vision, i.e., letting your smartphone camera see in 3D just like you can!
What is a 3D camera? It is a device that captures the 3D structure of a scene. Most cameras are 2D, meaning they are a projection of the scene onto the camera's imaging plane; any depth information is lost. However, a 3D camera also captures the depth dimension (in addition to the standard 2D data).
What can you do with a 3D picture?
- Capture models of objects or people for 3D printing or CAD modeling
- Make absolute 3D measurements from the photo - for, construction and remodeling, interior design, clothes shopping, etc.
- Remove objects or people outside a given depth - eliminate "photo bombers," remove the background scene from photos, replace the background scene
- Change the angle or lighting of the photo after the fact
- More easily perform a variety of photo editing ("photo-shopping") effects, with the aid of the image depth channel
- Implement augmented reality games, or play existing augmented reality games
- Much, much more - just search the web to see what people do with 3D sensing, and imagine how these applications could translate to or enhance mobile device apps
Additionally, by analogy with stitching 2D panoramas, It is also possible to stitch together multiple 3D views of an object for improved resolution and model quality. Here's an example of a multi-view stitched model:
Why should someone get LazeeEye? Because it is simple; because it is cheap. It is an add-on module that can work with any smartphone - all you need is a camera and some processing horsepower (iPhone or Android), and the LazeeEye stereo vision app can form the 3D picture. The size of the module is roughly the size of a a pencil (although our existing prototype is a little larger), and, once mass-produced, is projected to cost less than the current DIY reward level. Sure, we all know 3D camera phones are on the horizon (although none are available yet); but will you be able to get one for even under $500, let alone under $100? This is the kind of price point that truly can bring this technology to *everyone* - which, in turn, can drive a virtuous circle of innovations, applications, and business in the emerging world of 3D mobile.
Which version should I get? By popular demand, we've decrypted the pledge reward levels into this infographic to help you better understand the options:
The figure above shows the notional components of LazeeEye and how it integrates with existing phone hardware. Also shown is an example real-world scene and its computed depth image. Below is one simple way to visualize depth in a 3D image, using a "wiggle GIF" :
The apparent motion is related to the depth of each point or object in the scene - giving a sense of the 3D scene structure to a person viewing the image even on a clunky old 2D computer screen!
How does LazeeEye work? The enabling technology behind LazeeEye is active stereo vision, where (by analogy with human stereo vision) one "eye" is your existing smartphone camera and passively receives incoming light, while the other "eye" actively projects light outwards onto the scene, where it bounces back to the passive eye. The projected light is patterned in a way that is known and pre-calibrated in the smartphone; after snapping a photo, the stereo vision software on the phone can cross-reference this image with its pre-calibrated reference image. After finding feature matches between the current and reference image, the algorithm essentially triangulates to compute an estimate of the depth. It performs this operation for each pixel, ultimately yielding a high-resolution depth image that matches pixel-for-pixel with the standard 2D color image (equivalently, this can be considered a colored 3D point cloud). Note that LazeeEye also performs certain temporal modulation "magic" (the details of which we're carefully guarding as a competitive advantage) that boosts the observed signal-to-noise ratio, allowing the projected pattern to appear much brighter against the background.
Note that a more in-depth treatment of active stereo vision can be found in the literature: e.g., http://www.willowgarage.com/sites/default/files/ptext.pdf and https://cvhci.anthropomatik.kit.edu/~manel/publications/mva2013RGBD.pdf
The image above shows an early, but functional, prototype of LazeeEye. For both backer rewards and mass manufacture, a smoother look-and-feel with likely smaller components will be used (as per the trend below):
LazeeEye in Popular Tech Press
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By connecting to existing services and software partners, LazeeEye can have a more immediate and broad impact out-of-the-box.
For example, SketchFab is a popular 3D model sharing site with hundreds of thousands of users; LazeeEye will export directly to SketchFab, allowing you to easily share your 3D pictures and models with anyone using a modern web browser. For an example model, see top of this page: "Do Girls Dream in 3D?"
On the computer vision front, Heuristic Labs will be working closely with the surging start-up vision.ai to accomplish 3D reconstruction (multi-view stitching) and object detection algorithms using the power of cloud computing - of course, all designed to work seamlessly with data coming out of the LazeeEye 3D camera.
We're also exploring partnerships with end-user applications, such as the nifty 3D body scanning system (e.g., for precise clothing measurements) produced by Size Stream.
How is LazeeEye different from existing, similar products? In short, it is smaller and it is cheaper... MUCH cheaper. It uses existing components that are already mass-manufactured (i.e., CHEAP) for the laser illuminator, and - this is the key insight - it utilizes the low-power/light-weight processing and high-resolution camera that already exists in millions of smartphones, so YOU DON'T HAVE TO BUY ANOTHER PHONE! Just upgrade the one you have, for pennies on the dollar. For a partial list of specific comparisons to related technologies, see below.
- Project Tango (https://www.google.com/atap/projecttango/) was recently announced by Google. It requires an entirely new phone with a custom processing chip (selling price not yet disclosed, but not likely to be cheaper than existing flagship smartphones at ~$500) and focuses on mapping applications; whereas LazeeEye uses your existing smartphone, costs 10x less, and focuses on 3D photo snapshot and object modeling applications.
- The Microsoft Kinect (http://en.wikipedia.org/wiki/Kinect) uses a custom system-on-chip image processor, costs ~$150, is much larger/heavier/power-hungry than a smartphone, must be tethered to a PC, and is focused on skeleton tracking applications
- The PrimeSense Capri sensor is used in the Microsoft Kinect, and so the previous compare/contrast bullet also applies; also note that PrimeSense was recently acquired by Apple.
- The Structure Sensor from Occipital (https://www.kickstarter.com/projects/occipital/structure-sensor-capture-the-world-in-3d) is an add-on module for iOS mobile devices, specifically iPad-sized; as it includes its own camera and processing, it is much more expensive (~$250), while still needing to transmit the data to the mobile device, ultimately limiting it to supporting only iOS devices in the iPad size class.
- Scanning laser range finders are larger, more expensive (typically $5000 or more), and only give a planar slice of range data, typically constituting 10x fewer range readings.
- Passive stereo cameras (e.g., https://www.kickstarter.com/projects/935366406/poppy-turn-your-iphone-into-a-3d-camera-0) tend to have holes in the depth image, especially in dull environments (like blank white walls) or environments with repeated texture (like trees in a forest). Active stereo vision does not suffer this drawback, as it projects its own carefully-structured features onto the environment.
- 2D photos constitute a fundamentally lossy representation of our 3D world, making automated perception (required for advanced photo editing and effects), as well as size measurements, difficult or impossible.
Above, we show a project plan for developing LazeeEye, delivering rewards to backers, and post-project plans. Of particular note are the June and December milestones, when we'll be shipping prototype units and developer library API, respectively.
Although the rewards we're offering in this campaign can't reflect the economies-of-scale unit price under mass production, we believe there is still tremendous value in being early supporters, backing our project, receiving a prototype of one of these cool devices, capturing scenes in all their 3D glory, using the device's data in your own software app or artistic projects, generally becoming the envy of your friends and family... and, most importantly, proudly counting yourself amongst those who helped bootstrap the 3D revolution!
Risks and challenges
UPDATE: to remove risk or uncertainty for backers, we can promise a full refund at any time (given you return device in working order if you want refund after you've already received it).
The main risk to completing the project is not achieving the accuracy, resolution, or robustness to lighting conditions needed to meet all use cases. Currently, we have built a proof-of-concept prototype in hardware and have implemented the depth estimation routine on a PC; we've tested the algorithm on real sensor data collected from this prototype. Early results are encouraging, instilling confidence that our technique can do roughly as well as more-expensive or less-mobile competitors. In any case, we are certain that we will be able to deliver a functioning prototype to backers - the hardest part has been done already, and now we just need to tweak some parameters to maximize performance.
Note that backers may rest assured that we know what we're doing, that this is not our first rodeo, and that we can deliver: Heuristic Labs comprises decades of aggregate experience in these technologies, members having worked widely as research scientists, engineers, and designers at institutions such as NASA, MIT, Microsoft Research, IBM Research, Maya Design, Carnegie Mellon University, and others. See our full bio (link at upper right) for more details on the Heuristic Labs story.Learn about accountability on Kickstarter
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