So, can we somehow get away with using much fewer pixels, say below 1% of our total 3.17MP, to extract the markers' 2D positions? The answer is yes, but we will need to encode the 2D positions in a novel way, using light diffraction. In fact, in the scene above our two LEDs might occupy under 200 pixels of our 3.17MP camera. Looking at the high-speed camera frames above suggests that the answer is no, since our light-source markers occupy the image domain very sparsely. So what can we do? Well, here is a question: do we really need to capture all the image pixels? Moreover, even it could, that memory would run out very fast. What's the problem then? it's bandwidth at frame hight rates, your camera can't transfer all the image data from the sensor to its memory fast enough.
Light diffraction gif manual#
Even your smartphone camera can easily take images at a 1/8000 exposure (try it yourself in manual mode). Such high frame rates require very short exposures, but these are easy to do. How fast? thousands of frames-per-second fast. Now suppose we want to track these markers fast. The problem is bandwidth Bring motion to your designs or projects.
Light diffraction gif for free#
Thus, our system is able to achieve high-speed and high-accuracy 2D positioning of light sources and 3D scanning of scenes. Download 1 Diffraction Symbol Lottie Animations for free or premium in JSON, LOTTIE, GIF, AEP or MP4 formats. Line imaging has several advantages over 2D sensors: high frame rate, high dynamic range, high fill-factor with additional on-chip computation, low cost beyond the visible spectrum, and high energy efficiency when used with line illumination. We demonstrate our approach in two applications: (a) fast passive imaging of sparse light sources like street lamps, headlights at night and LED-based motion capture, and (b) structured light 3D scanning with line illumination and line sensing. Diffuse reflection is responsible for the ability to see most illuminated surfaces from any positionrays reach the eyes after reflecting off every portion of the surface.
We show several extensions of our system to improve the accuracy of the 2D positioning and expand the effective field of view. We derive a detailed image formation model and a learning-based algorithm for 2D position estimation. We exploit this principle to recover 2D and even 3D positions from only line images.
When light passes through a diffraction grating, it disperses as a function of wavelength. We present a novel computational imaging principle that combines diffractive optics with line (1D) sensing.
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