OPTAR: Automatic Coordinate Frame Registration between OpenPTrack and Google ARCore using Ambient Visual Features

This thesis presents a system for the estimation of the coordinate frame registration between OpenPTrack and Google ARCore. OpenPTrack is a multi-camera solution that integrates people tracking, skeleton tracking, and pose recognition. ARCore is a framework for the development of Augmented Reality applications on smartphones. The transformation between the two coordinate frames is obtained by exploiting visual features observed by both the phone and OpenPTrack cameras

3-D Hand Pose Estimation from Kinect's Point Cloud Using Appearance Matching

We present a novel appearance-based approach for pose estimation of a human hand using the point clouds provided by the low-cost Microsoft Kinect sensor. Both the free-hand case, in which the hand is isolated from the surrounding environment, and the hand-object case, in which the different types of interactions are classified, have been considered. The hand-object case is clearly the most challenging task having to deal with multiple tracks. The approach proposed here belongs to the class of partial pose estimation where the estimated pose in a frame is used for the initialization of the next one. The pose estimation is obtained by applying a modified version of the Iterative Closest Point (ICP) algorithm to synthetic models to obtain the rigid transformation that aligns each model with respect to the input data. The proposed framework uses a "pure" point cloud as provided by the Kinect sensor without any other information such as RGB values or normal vector components. For this reason, the proposed method can also be applied to data obtained from other types of depth sensor, or RGB-D camera

Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling

We study 3D shape modeling from a single image and make contributions to it in three aspects. First, we present Pix3D, a large-scale benchmark of diverse image-shape pairs with pixel-level 2D-3D alignment. Pix3D has wide applications in shape-related tasks including reconstruction, retrieval, viewpoint estimation, etc. Building such a large-scale dataset, however, is highly challenging; existing datasets either contain only synthetic data, or lack precise alignment between 2D images and 3D shapes, or only have a small number of images. Second, we calibrate the evaluation criteria for 3D shape reconstruction through behavioral studies, and use them to objectively and systematically benchmark cutting-edge reconstruction algorithms on Pix3D. Third, we design a novel model that simultaneously performs 3D reconstruction and pose estimation; our multi-task learning approach achieves state-of-the-art performance on both tasks.Comment: CVPR 2018. The first two authors contributed equally to this work. Project page: http://pix3d.csail.mit.ed

Feature Based Calibration of a Network of Kinect Sensors

The availability of affordable depth sensors in conjunction with common RGB cameras, such as the Microsoft Kinect, can provide robots with a complete and instantaneous representation of the current surrounding environment. However, in the problem of calibrating multiple camera systems, traditional methods bear some drawbacks, such as requiring human intervention. In this thesis, we propose an automatic and reliable calibration framework that can easily estimate the extrinsic parameters of a Kinect sensor network. Our framework includes feature extraction, Random Sample Consensus and camera pose estimation from high accuracy correspondences. We also implement a robustness analysis of position estimation algorithms. The result shows that our system could provide precise data under certain amount noise. Keywords Kinect, Multiple Camera Calibration, Feature Points Extraction, Correspondence, RANSA

Deep Learning for Head Pose Estimation: A Survey

Head pose estimation (HPE) is an active and popular area of research. Over the years, many approaches have constantly been developed, leading to a progressive improvement in accuracy; nevertheless, head pose estimation remains an open research topic, especially in unconstrained environments. In this paper, we will review the increasing amount of available datasets and the modern methodologies used to estimate orientation, with a special attention to deep learning techniques. We will discuss the evolution of the feld by proposing a classifcation of head pose estimation methods, explaining their advantages and disadvantages, and highlighting the diferent ways deep learning techniques have been used in the context of HPE. An in-depth performance comparison and discussion is presented at the end of the work. We also highlight the most promising research directions for future investigations on the topic