What if devices could see through walls, boxes, and clutter?
We’ve developed mmNorm, a new technology that creates 3D models of objects—even when they’re completely hidden from view. While traditional cameras and LiDAR systems can only detect and recontruct what’s in their direct line of sight, mmNorm uses millimeter-wave (mmWave) radar—the same kind of wireless signal used in 5G networks and airport scanners—that can pass through common materials like cardboard, fabric, and plastic.
This technology could enable robots to find and pick up items inside closed containers, allow AR headsets to reveal objects behind furniture, and help smart devices understand gestures even when users are out of sight.
How does it work?
Instead of simply measuring the strength of radar reflections (as past methods do), mmNorm estimates the curvature of hidden objects by analyzing how radar waves bounce off them. This allows it to reconstruct the object's shape with much greater accuracy.
Here’s the process:
- Estimate Surface Normals
mmNorm determines which direction each part of the hidden object surface is facing, based on the patterns of radar reflections. - Reconstruct the Surface Candidates
It then pieces together these surface directions to form multiple surface candidates for the object's shape. - Optimize the Result
Finally, mmNorm simulates how different 3D shapes candidate would reflect radar signals and selects the one that best matches the actual radar measurements.
We tested mmNorm on over 60 everyday objects—including mugs, tools, and toys—hidden behind boxes and clutter. You can see examples of these reconstructions in our video: