Real-World Normal Map Capture for Nearly Flat Reflective Surfaces

Although specular objects have gained interest in recent years, virtually no approaches exist for markerless reconstruction of reflective scenes in the wild. In this work, we present a practical approach to capturing normal maps in real-world scenes using video only. We focus on nearly planar surfaces such as windows, facades from glass or metal, or frames, screens and other indoor objects and show how normal maps of these can be obtained without the use of an artificial calibration object. Rather, we track the reflections of real-world straight lines, while moving with a hand-held or vehicle-mounted camera in front of the object. In contrast to error-prone local edge tracking, we obtain the reflections by a robust, global segmentation technique of an ortho-rectified 3D video cube that also naturally allows efficient user interaction. Then, at each point of the reflective surface, the resulting 2D-curve to 3D-line correspondence provides a novel quadratic constraint on the local surface normal. This allows to globally solve for the shape by integrability and smoothness constraints and easily supports the usage of multiple lines. We demonstrate the technique on several objects and facades

A framework for realistic 3D tele-immersion

Meeting, socializing and conversing online with a group of people using teleconferencing systems is still quite differ- ent from the experience of meeting face to face. We are abruptly aware that we are online and that the people we are engaging with are not in close proximity. Analogous to how talking on the telephone does not replicate the experi- ence of talking in person. Several causes for these differences have been identified and we propose inspiring and innova- tive solutions to these hurdles in attempt to provide a more realistic, believable and engaging online conversational expe- rience. We present the distributed and scalable framework REVERIE that provides a balanced mix of these solutions. Applications build on top of the REVERIE framework will be able to provide interactive, immersive, photo-realistic ex- periences to a multitude of users that for them will feel much more similar to having face to face meetings than the expe- rience offered by conventional teleconferencing systems

Grasping unknown objects in clutter by superquadric representation

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, a quick and efficient method is presented for grasping unknown objects in clutter. The grasping method relies on real-time superquadric (SQ) representation of partial view objects and incomplete object modelling, well suited for unknown symmetric objects in cluttered scenarios which is followed by optimized antipodal grasping. The incomplete object models are processed through a mirroring algorithm that assumes symmetry to first create an approximate complete model and then fit for SQ representation. The grasping algorithm is designed for maximum force balance and stability, taking advantage of the quick retrieval of dimension and surface curvature information from the SQ parameters. The pose of the SQs with respect to the direction of gravity is calculated and used together with the parameters of the SQs and specification of the gripper, to select the best direction of approach and contact points. The SQ fitting method has been tested on custom datasets containing objects in isolation as well as in clutter. The grasping algorithm is evaluated on a PR2 robot and real time results are presented. Initial results indicate that though the method is based on simplistic shape information, it outperforms other learning based grasping algorithms that also work in clutter in terms of time-efficiency and accuracy.Peer ReviewedPostprint (author's final draft

Precise motion descriptors extraction from stereoscopic footage using DaVinci DM6446

A novel approach to extract target motion descriptors in multi-camera video surveillance systems is presented. Using two static surveillance cameras with partially overlapped field of view (FOV), control points (unique points from each camera) are identified in regions of interest (ROI) from both cameras footage. The control points within the ROI are matched for correspondence and a meshed Euclidean distance based signature is computed. A depth map is estimated using disparity of each control pair and the ROI is graded into number of regions with the help of relative depth information of the control points. The graded regions of different depths will help calculate accurately the pace of the moving target and also its 3D location. The advantage of estimating a depth map for background static control points over depth map of the target itself is its accuracy and robustness to outliers. The performance of the algorithm is evaluated in the paper using several test sequences. Implementation issues of the algorithm onto the TI DaVinci DM6446 platform are considered in the paper