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Researchers enable today’s vehicle and smartphone cameras to see around corners

Algolux and University Scientists Develop Non-Line-of-Sight Technology That Photographs Hidden Objects Using Conventional Camera Sensors
Conference on Computer Vision and Pattern Recognition (CVPR),

Algolux has announced that a team of researchers from Algolux, the University of Montreal, and Princeton University has developed a new method that lets conventional color cameras — the ones in your smartphone or in a vehicle camera — see hidden objects that are occluded by walls or other scene objects.

Reconstruction of traffic signs with high-resolution color Non-Line-Of-Sight imaging using conventional CMOS cameras sensors.
Reconstruction of traffic signs with high-resolution color Non-Line-Of-Sight imaging using conventional CMOS cameras sensors.

The team achieved unprecedented resolution for non-line-of-sight (NLOS) imaging by being able to see objects in high-resolution and color around corners for the first time. The researchers from academia and industry were able to reconstruct high quality images of traffic signs (see Figure 1) and other 3D objects without looking directly at those objects.

The authors were able to accomplish these results with existing conventional CMOS camera sensors and a change in illumination method, needing only a small change to a car’s headlights or a smartphone’s flash. This research breakthrough opens a path to practical implementation across numerous markets.

Example of applying high-resolution Non-Line-Of-Sight imaging for a real-world driving application.
Example of applying high-resolution Non-Line-Of-Sight imaging for a real-world driving application

Specifically, Algolux believes this technology can further strengthen the ability for autonomous vehicles to safely navigate in difficult road scenarios even when the view is blocked by obstructions or vehicles, deliver increased security for video surveillance, and enable additional use cases for smartphones, augmented reality, and medical imaging (Figure 2).

The findings will be published at the prestigious CVPR computer vision conference this week and were chosen to be presented as one of the select few oral presentations.

More details on this research can be found on the dedicated project page at CS PRINCETON.

For information about Algolux’s research projects, please visit Algolux Research.

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