Occlusion Handling using Semantic Segmentation and Visibility-Based Rendering for Mixed Reality

Real-time occlusion handling is a major problem in outdoor mixed reality system because it requires great computational cost mainly due to the complexity of the scene. Using only segmentation, it is difficult to accurately render a virtual object occluded by complex objects such as trees, bushes etc. In this paper, we propose a novel occlusion handling method for real-time, outdoor, and omni-directional mixed reality system using only the information from a monocular image sequence. We first present a semantic segmentation scheme for predicting the amount of visibility for different type of objects in the scene. We also simultaneously calculate a foreground probability map using depth estimation derived from optical flow. Finally, we combine the segmentation result and the probability map to render the computer generated object and the real scene using a visibility-based rendering method. Our results show great improvement in handling occlusions compared to existing blending based methods

The Evolution of First Person Vision Methods: A Survey

The emergence of new wearable technologies such as action cameras and smart-glasses has increased the interest of computer vision scientists in the First Person perspective. Nowadays, this field is attracting attention and investments of companies aiming to develop commercial devices with First Person Vision recording capabilities. Due to this interest, an increasing demand of methods to process these videos, possibly in real-time, is expected. Current approaches present a particular combinations of different image features and quantitative methods to accomplish specific objectives like object detection, activity recognition, user machine interaction and so on. This paper summarizes the evolution of the state of the art in First Person Vision video analysis between 1997 and 2014, highlighting, among others, most commonly used features, methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart Glasses, Computer Vision, Video Analytics, Human-machine Interactio

Co-Fusion: Real-time Segmentation, Tracking and Fusion of Multiple Objects

In this paper we introduce Co-Fusion, a dense SLAM system that takes a live stream of RGB-D images as input and segments the scene into different objects (using either motion or semantic cues) while simultaneously tracking and reconstructing their 3D shape in real time. We use a multiple model fitting approach where each object can move independently from the background and still be effectively tracked and its shape fused over time using only the information from pixels associated with that object label. Previous attempts to deal with dynamic scenes have typically considered moving regions as outliers, and consequently do not model their shape or track their motion over time. In contrast, we enable the robot to maintain 3D models for each of the segmented objects and to improve them over time through fusion. As a result, our system can enable a robot to maintain a scene description at the object level which has the potential to allow interactions with its working environment; even in the case of dynamic scenes.Comment: International Conference on Robotics and Automation (ICRA) 2017, http://visual.cs.ucl.ac.uk/pubs/cofusion, https://github.com/martinruenz/co-fusio

Detection of Features to Track Objects and Segmentation Using GrabCut for Application in Marker-less Augmented Reality

AbstractAugmented Reality applications have hovered itself over various platforms such as desktop and most recently to handheld devices such as mobile phones and tablets. Augmented Reality (AR) systems have mostly been limited to Head Worn Displays with start-ups such as Magic Leap and Occulus Rift making tremendous advancement in such AR and VR research applications facing a stiff competition with Software giant Microsoft which has recently introduced Holo Lens. AR refers to the augmentation or the conglomeration of virtual objects in the real world scenario which has a distinct but close resemblance to Virtual Reality (VR) systems which are computer simulated environments which render physical presence in imaginary world. Developers and hackers round the globe have directed their research interests in the development of AR and VR based applications especially in the domain of advertisement and gaming. Many open source libraries, SDKs and proprietary software are available worldwide for developers to make such systems. This paper describes an algorithm for an AR prototype which uses a marker less approach to track and segment out real world objects and then overlay the same on another real world scene. The algorithm was tested on Desktop. The results are comparable with other existing algorithms and outperform some of them in terms of robustness, speed, and accuracy, precision and timing analysis

RGB-D datasets using microsoft kinect or similar sensors: a survey

RGB-D data has turned out to be a very useful representation of an indoor scene for solving fundamental computer vision problems. It takes the advantages of the color image that provides appearance information of an object and also the depth image that is immune to the variations in color, illumination, rotation angle and scale. With the invention of the low-cost Microsoft Kinect sensor, which was initially used for gaming and later became a popular device for computer vision, high quality RGB-D data can be acquired easily. In recent years, more and more RGB-D image/video datasets dedicated to various applications have become available, which are of great importance to benchmark the state-of-the-art. In this paper, we systematically survey popular RGB-D datasets for different applications including object recognition, scene classification, hand gesture recognition, 3D-simultaneous localization and mapping, and pose estimation. We provide the insights into the characteristics of each important dataset, and compare the popularity and the difficulty of those datasets. Overall, the main goal of this survey is to give a comprehensive description about the available RGB-D datasets and thus to guide researchers in the selection of suitable datasets for evaluating their algorithms

A mixed reality telepresence system for collaborative space operation

This paper presents a Mixed Reality system that results from the integration of a telepresence system and an application to improve collaborative space exploration. The system combines free viewpoint video with immersive projection technology to support non-verbal communication, including eye gaze, inter-personal distance and facial expression. Importantly, these can be interpreted together as people move around the simulation, maintaining natural social distance. The application is a simulation of Mars, within which the collaborators must come to agreement over, for example, where the Rover should land and go. The first contribution is the creation of a Mixed Reality system supporting contextualization of non-verbal communication. Tw technological contributions are prototyping a technique to subtract a person from a background that may contain physical objects and/or moving images, and a light weight texturing method for multi-view rendering which provides balance in terms of visual and temporal quality. A practical contribution is the demonstration of pragmatic approaches to sharing space between display systems of distinct levels of immersion. A research tool contribution is a system that allows comparison of conventional authored and video based reconstructed avatars, within an environment that encourages exploration and social interaction. Aspects of system quality, including the communication of facial expression and end-to-end latency are reported