Synthetically creating motion blur in two-dimensional (2D) images is a well-understood process and has been used in image processing for developing deblurring systems. There are no well-established techniques for synthetically generating arbitrary motion blur within three-dimensional (3D) images, such as depth maps and point clouds since their behavior is not as well understood. As a prerequisite, we have previously developed a method for generating synthetic motion blur in a plane that is parallel to the sensor detector plane. In this work, as a major extension, we generalize our previously developed framework for synthetically generating linear and radial motion blur along planes that are at arbitrary angles with respect to the sensor detector plane. Our framework accurately captures the behavior of the real motion blur that is encountered using a Time-of-Flight (ToF) sensor. This work uses a probabilistic model that predicts the location of invalid pixels that are typically present within depth maps that contain real motion blur. More specifically, the probabilistic model considers different angles of motion paths and the velocity of an object with respect to the image plane of a ToF sensor. Extensive experimental results are shown that demonstrate how our framework can be applied to synthetically create radial, linear, and combined radial-linear motion blur. We quantify the accuracy of the synthetic generation method by comparing the resulting synthetic depth map to the experimentally captured depth map with motion. Our results indicate that our framework achieves an average Boundary F1 (BF) score of 0.7192 for invalid pixels for synthetic radial motion blur, an average BF score of 0.8778 for synthetic linear motion blur, and an average BF score of 0.62 for synthetic combined radial-linear motion blur.

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.

One of the challenges of using Time-of-Flight (ToF) sensors for dimensioning objects is that the depth information suffers from issues such as low resolution, self-occlusions, noise, and multipath interference, which distort the shape and size of objects. In this work, we successfully apply a superquadric fitting framework for dimensioning cuboid and cylindrical objects from point cloud data generated using a ToF sensor. Our work demonstrates that an average error of less than 1 cm is possible for a box with the largest dimension of about 30 cm and a cylinder with the largest dimension of about 20 cm that are each placed 1.5 m from a ToF sensor. We also quantify the performance of dimensioning objects using various object orientations, ground plane surfaces, and model fitting methods. For cuboid objects, our results show that the proposed superquadric fitting framework is able to achieve absolute dimensioning errors between 4% and 9% using the bounding technique and between 8% and 15% using the mirroring technique across all tested surfaces. For cylindrical objects, our results show that the proposed superquadric fitting framework is able to achieve absolute dimensioning errors between 2.97% and 6.61% when the object is in a horizontal orientation and between 8.01% and 13.13% when the object is in a vertical orientation using the bounding technique across all tested surfaces.