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Computer vision
3d Vision

The improvement of stereo depth accuracy

Light hero image

The overview

Light is a Silicon Valley-based startup that uses computational imaging to enable machines to see like humans. We worked with Light to improve the accuracy of their stereo depth and sensor geometric calibration. With support from Softbank’s Vision Fund, Light has become the world’s most advanced imaging platform, working across a range of applications from self-driving vehicles to robotics and smart cities.

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  • C++
  • Python
  • Blender, Unreal Engine


  • Camera calibration
  • Nonlinear optimization
  • Stereo vision
  • 3D Modelling

The problem

Light’s product is a software-defined camera that combines breakthrough optics technology with sophisticated computational software. Apart from creating more vivid and more detailed HDR images, it uses stereo vision and calculates the depth of the image.

Accuracy of depth is an important part of providing high-quality, flawless images. Proper sensor geometric calibration is also critical as individual cameras are not glued to any surface and are prone to micro movements when the device is used.

Project goals

The solution

The project was divided into 3 parts: online calibration research, lense distortion correction research, and depth pipeline improvements. First, we focused on the theoretical and practical advantages of using online calibration, then we worked on improving the quality of stereo depth reconstruction

The following deliverables were defined and successfully implemented:

Project results

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