Probabilistic Modeling of Multicamera Interference for Time-of-Flight Sensors

Abstract

The behavior of multicamera interference in 3D images (e.g., depth maps), which is based on infrared (IR) light, is not well understood. In 3D images, when multicamera interference is present, there is an increase in the amount of zero-value pixels, resulting in a loss of depth information. In this work, we demonstrate a framework for synthetically generating direct and indirect multicamera interference using a combination of a probabilistic model and ray tracing. Our mathematical model predicts the locations and probabilities of zero-value pixels in depth maps that contain multicamera interference. Our model accurately predicts where depth information may be lost in a depth map when multicamera interference is present. We compare the proposed synthetic 3D interference images with controlled 3D interference images captured in our laboratory. The proposed framework achieves an average root mean square error (RMSE) of 0.0625, an average peak signal-to-noise ratio (PSNR) of 24.1277 dB, and an average structural similarity index measure (SSIM) of 0.9007 for predicting direct multicamera interference, and an average RMSE of 0.0312, an average PSNR of 26.2280 dB, and an average SSIM of 0.9064 for predicting indirect multicamera interference. The proposed framework can be used to develop and test interference mitigation techniques that will be crucial for the successful proliferation of these devices.

Dimensioning Cuboid and Cylindrical Objects Using Only Noisy and Partially Observed Time-of-Flight Data

“…This work presents a method for dimensioning cuboid and cylindrical objects from noisy and partially occluded point cloud data that are acquired using a Time-of-Flight (ToF) sensor. Over the last decade, there has been an increase in the types of applications where ToF sensors are used, such as 3D scanning [1][2][3][4][5], drone positioning [6][7][8], robotics [9][10][11], and logistics [12][13][14]. In applications such as metrology and logistics, the ability to accurately determine the dimensions of an object is critical, for example, for part picking, packaging, and estimating shipping costs and storage needs.…”