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Imaging Hidden Objects With Consumer Lidar Via Motion-induced Sampling.

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Imaging Hidden Objects With Consumer Lidar Via Motion-induced Sampling.

Related Experiment Video

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

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Imaging hidden objects with consumer LiDAR via motion-induced sampling.

Siddharth Somasundaram¹, Aaron Young², Akshat Dave²

¹Massachusetts Institute of Technology, Cambridge, MA, USA. sidsoma@media.mit.edu.

|May 20, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

Researchers developed a new method to enable non-line-of-sight (NLOS) imaging using consumer-grade Light-Detection and Ranging (LiDAR) devices. This breakthrough allows everyday users to capture images of hidden objects with affordable, off-the-shelf technology.

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Determining 3D Flow Fields via Multi-camera Light Field Imaging

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Area of Science:

Computer Vision
Optics
Robotics

Background:

Consumer Light-Detection and Ranging (LiDAR) sensors are increasingly used in various applications.
Existing LiDAR technology faces challenges in non-line-of-sight (NLOS) imaging due to signal quality, motion, and resolution limitations.

Purpose of the Study:

To develop a method for achieving NLOS imaging using consumer-grade LiDAR devices.
To overcome the limitations of current consumer LiDAR for imaging hidden objects.

Main Methods:

Proposed a multi-frame fusion strategy to enhance signal quality.
Introduced a motion-induced aperture sampling model to unify object and camera motion effects.
Utilized smartphone-grade LiDAR for demonstrations.

Main Results:

Successfully demonstrated NLOS imaging capabilities on consumer LiDAR.
Achieved three-dimensional reconstruction, object tracking, and camera localization using hidden objects.
Showcased plug-and-play NLOS imaging with affordable, off-the-shelf hardware.

Conclusions:

The developed method democratizes NLOS imaging, making it accessible for consumer applications.
This advancement shifts NLOS imaging from specialized research hardware to readily available consumer devices.
The findings pave the way for broader adoption of NLOS imaging in handheld, wearable, and robotic systems.