Egozi A, Eilot D, Maass P, and Sagiv C: A robust estimation method for camera calibration with known rotation.
Applied Mathematics, 6(9): 1538-1552, 2015.


Egozi, A., Maass, P., Sagiv,C.: A robust estimation method for camera calibration with known rotation.
Proceedings in Applied Mathematics and Mechanics, 15(1): 657-658, 2015.


Pham Q M, Nho Hào D, Maass P, Pidcock M: Descent Gradient Methods for Nonsmooth Minimization Problems and Applications to Ill-Posed Problems.
Journal of Computational and Applied Mathematics, 298: 105-122, 2016.


Schoenenberger Y, Paratte J, Vandergheynst P: Graph-based denoising for time-varying point clouds.
Published in: 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON), 2015.



Conference contributions

December 10th 2014 – Chen Sagiv, talk on "SceneNet to the algorithms group at Orbotech".


December 1st 2014 – Chen Sagiv & Eri Rubin, VP R&D, talk at the Compiler, Architecture and Tools Conference in Haifa on "State of the Art Architectures for Machine Vision tasks: from Tango to SceneNet".


November 5th 2014 – Chen Sagiv, talk on "Computer Vision on Tegra K1":,


September 17th 2014 – Chen Sagiv, talk at the Heterogeneous Computing workshop in the Hebrew University of Jerusalem (organized by Prof. Amnon Barak).


June 9th 2014 – Oren Tropp, talk on the System Day at the Technion Institute in Haifa on: "Take GPU Power to the Maximum for Vision and Depth Sensor Processing: From Mobile GPU to GPUs on the Cloud"


April 1st 2014 – SagivTech’s booth at IMVC (Israel Machine Vision Conference) 2014 presenting SceneNet and demos based on the SceneNet infrastructure.


March 25th 2014 – Chen Sagiv & Eri Rubin, VP R&D, talk at the NVIDIA yearly GTC (GPU Technology Conference, San Jose: "From Google Project Tango to SceneNet".


Over the last six months SagivTech has been intensively developing CUDA code on the K1 Tegra for mobile computer vision applications that require immense computing power. In this talk we will share our joint effort together with NVIDIA and Mantis Vision to port the core algorithms of Mantis Vision's depth camera to NVIDIA K1 Tegra. We will also introduce SceneNet, a project funded by the European Commission that uses the power of crowd sourcing, in the form of multiple mobile phone users, to create a higher quality 3D video scene experience. We will discuss SagivTech's vision to exploit the compute power of the hybrid platform composed of NVIDIA's K1 Tegra and discrete GPUs in the cloud for computationally intensive, online and interactive applications. We will conclude with some take home tips on writing CUDA on the K1 Tegra (



OpenCL application on Mobile GPUs:


Tutorial - How to build from scratch an android application using JNI, OpenCV and OpenCl under Linux:



This project is funded by the European Union under the 7th Research Framework, programme FET-Open SME,
Grant agreement no. 309169.
7th Research Framework programme FET-Open SME, Grant agreement no. 309169