Thursday, May 16, 2019

MultiVu Raises $7M for 3D FaceID with Single Camera

PRNewswire, CTech: Isreal-based MultiVu, developing 3D imaging solutions using a single sensor and deep learning derived algorithms, announces the completion of a $7M seed round led by OurCrowd, Cardumen Capital and Hong Kong based investment firm Junson Capital. MultiVu will use the funding to complete development of its first sensor product for 3D Face Authentication applications.

MultiVu's 3D image camera is based on a single sensor as opposed to existing solutions using two sensors and a light projector. The solution produces both pictures and video streams as needed. MultiVu's single sensor solution is promised to be inexpensive, compact and energy efficient. MultiVu method is based on state of the art deep learning algorithms, which makes it a 100% passive solution (no illumination), operating as a front facing camera, using depth and RGB data, all in a single shot.

Doron Nevo, CEO of MultiVu said "The technology, which passed the proof-of-concept stage will bring 3D Face Authentication and affordable 3D imaging to the mobile, automotive, industrial and medical markets. We are excited to be given the opportunity to commercialize this technology."

MultiVu's technology is based on four years of research conducted by David Mendlovic and his team from the Tel Aviv University.


  1. 3D FaceID with Single Camera - is this 3D reliable? or use video sequence?

  2. just another company doing another work on face ID having another opinion.
    In the end, what is the relevance of this article?

    1. The relevance is that it's a 3D imaging with a single camera. How do the do it? There is some blurb on lightfields but no real explanation

  3. I believe this paper describes their solution. Note that the sensor used should have no standard RGB CFA but a random mask placed at a distance from the sensor. Also, if this camera would be used for standard picture taking, they'd need to develop a complete camera, quite an undertaking!
    Here is a similar approach which also compares performance to that of MultiVu.


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