Reducing interference between multiple structured light depth sensors using motion

TLDR

The authors propose a technique to mitigate interference among multiple structured‑light depth sensors sharing the same spectrum by introducing controlled motion to the sensors’ projectors and cameras. By applying a small vibration to a subset of sensors, each unit captures its own high‑frequency pattern sharply while the patterns from other units appear blurred, simplifying pattern disambiguation and enabling analysis on overlapping Microsoft Kinect arrays. Experiments with six Kinects reduced interference‑related missing data from 16.6 % to 1.4 % and with three Kinects cut measurement error by 82.2 %, demonstrating that the method enables dense large‑scale capture with commodity sensors, though color blur is mitigated by post‑processing.

Abstract

We present a method for reducing interference between multiple structured light-based depth sensors operating in the same spectrum with rigidly attached projectors and cameras. A small amount of motion is applied to a subset of the sensors so that each unit sees its own projected pattern sharply, but sees a blurred version of the patterns of other units. If high spacial frequency patterns are used, each sensor sees its own pattern with higher contrast than the patterns of other units, resulting in simplified pattern disambiguation. An analysis of this method is presented for a group of commodity Microsoft Kinect color-plus-depth sensors with overlapping views. We demonstrate that applying a small vibration with a simple motor to a subset of the Kinect sensors results in reduced interference, as manifested as holes and noise in the depth maps. Using an array of six Kinects, our system reduced interference-related missing data from from 16.6% to 1.4% of the total pixels. Another experiment with three Kinects showed an 82.2% percent reduction in the measurement error introduced by interference. A side-effect is blurring in the color images of the moving units, which is mitigated with post-processing. We believe our technique will allow inexpensive commodity depth sensors to form the basis of dense large-scale capture systems.