Improving remote collaborative process modelling using embodiment in 3D virtual environments

Identifying, modelling and documenting business processes usually require the collaboration of many stakeholders that may be spread across companies in inter-organizational settings. While modern process modelling technologies are beginning to provide a number of features to support remote, they lack support for visual cues used in co-located collaboration. In this paper, we examine the importance of visual cues for collaboration tasks in collaborative process modelling. Based on this analysis, we present a prototype 3D virtual world process modelling tool that supports a number of visual cues to facilitate remote collaborative process model creation and validation. We then report on a preliminary analysis of the technology. In conclusion, we proceed to describe the future direction of our research with regards to the theoretical contributions expected from the evaluation of the tool.

Collaborative 3D BPMN process modeller

A video detailing our new virtual world BPMN process modelling tool developed by Erik Poppe. Enables better situational awareness via use of remotely connected avatars and a shared 3D process diagram.

3D process visualisation configurations

Video detailing three process model visualisation configurations integrated into an agent driven virtual world simulation.

Coordination of UAV path planning in 2D and 3D in environmental/bio sensing applications

This paper is concerned with the optimal path planning and initialization interval of one or two UAVs in presence of a constant wind. The method compares previous literature results on synchronization of UAVs along convex curves, path planning and sampling in 2D and extends it to 3D. This method can be applied to observe gas/particle emissions inside a control volume during sampling loops. The flight pattern is composed of two phases: a start-up interval and a sampling interval which is represented by a semi-circular path. The methods were tested in four complex model test cases in 2D and 3D as well as one simulated real world scenario in 2D and one in 3D.

Prostate gland and extra-capsular tissue 3D reconstruction and measurement

Currently there are little objective parameters that can quantify the success of one form of prostate surgical removal over another. Accordingly, at Old Dominion University (ODU) we have been developing a process resulting in the use of software algorithms to assess the coverage and depth of extra-capsular soft tissue removed with the prostate by the various surgical approaches. Parameters such as the percent of capsule that is bare of soft tissue and where present the depth and extent of coverage have been assessed. First, visualization methods and tools are developed for images of prostate slices that are provided to ODU by the Pathology Department at Eastern Virginia Medical School (EVMS). The visualization tools interpolate and present 3D models of the prostates. Measurement algorithms are then applied to determine statistics about extra-capsular tissue coverage. This paper addresses the modeling, visualization, and analysis of prostate gland tissue to aid in quantifying prostate surgery success. Particular attention is directed towards the accuracy of these measurements and is addressed in the analysis discussions.

3D thermal mapping of building interiors using an RGB-D and thermal camera

The building sector is the dominant consumer of energy and therefore a major contributor to anthropomorphic climate change. The rapid generation of photorealistic, 3D environment models with incorporated surface temperature data has the potential to improve thermographic monitoring of building energy efficiency. In pursuit of this goal, we propose a system which combines a range sensor with a thermal-infrared camera. Our proposed system can generate dense 3D models of environments with both appearance and temperature information, and is the first such system to be developed using a low-cost RGB-D camera. The proposed pipeline processes depth maps successively, forming an ongoing pose estimate of the depth camera and optimizing a voxel occupancy map. Voxels are assigned 4 channels representing estimates of their true RGB and thermal-infrared intensity values. Poses corresponding to each RGB and thermal-infrared image are estimated through a combination of timestamp-based interpolation and a pre-determined knowledge of the extrinsic calibration of the system. Raycasting is then used to color the voxels to represent both visual appearance using RGB, and an estimate of the surface temperature. The output of the system is a dense 3D model which can simultaneously represent both RGB and thermal-infrared data using one of two alternative representation schemes. Experimental results demonstrate that the system is capable of accurately mapping difficult environments, even in complete darkness.

GrabCutSFM : how 3D information improves unsupervised object segmentation

In this paper, we present an unsupervised graph cut based object segmentation method using 3D information provided by Structure from Motion (SFM), called Grab- CutSFM. Rather than focusing on the segmentation problem using a trained model or human intervention, our approach aims to achieve meaningful segmentation autonomously with direct application to vision based robotics. Generally, object (foreground) and background have certain discriminative geometric information in 3D space. By exploring the 3D information from multiple views, our proposed method can segment potential objects correctly and automatically compared to conventional unsupervised segmentation using only 2D visual cues. Experiments with real video data collected from indoor and outdoor environments verify the proposed approach.

Nonparametric semantic segmentation for 3D street scenes

In this paper we propose a method to generate a large scale and accurate dense 3D semantic map of street scenes. A dense 3D semantic model of the environment can significantly improve a number of robotic applications such as autonomous driving, navigation or localisation. Instead of using offline trained classifiers for semantic segmentation, our approach employs a data-driven, nonparametric method to parse scenes which easily scale to a large environment and generalise to different scenes. We use stereo image pairs collected from cameras mounted on a moving car to produce dense depth maps which are combined into a global 3D reconstruction using camera poses from stereo visual odometry. Simultaneously, 2D automatic semantic segmentation using a nonparametric scene parsing method is fused into the 3D model. Furthermore, the resultant 3D semantic model is improved with the consideration of moving objects in the scene. We demonstrate our method on the publicly available KITTI dataset and evaluate the performance against manually generated ground truth.

Liveness detection based on 3D face shape analysis

In recent years face recognition systems have been applied in various useful applications, such as surveillance, access control, criminal investigations, law enforcement, and others. However face biometric systems can be highly vulnerable to spoofing attacks where an impostor tries to bypass the face recognition system using a photo or video sequence. In this paper a novel liveness detection method, based on the 3D structure of the face, is proposed. Processing the 3D curvature of the acquired data, the proposed approach allows a biometric system to distinguish a real face from a photo, increasing the overall performance of the system and reducing its vulnerability. In order to test the real capability of the methodology a 3D face database has been collected simulating spoofing attacks, therefore using photographs instead of real faces. The experimental results show the effectiveness of the proposed approach.

Advantages of exploiting projection structure for segmenting dense 3D point clouds

Timely and comprehensive scene segmentation is often a critical step for many high level mobile robotic tasks. This paper examines a projected area based neighbourhood lookup approach with the motivation towards faster unsupervised segmentation of dense 3D point clouds. The proposed algorithm exploits the projection geometry of a depth camera to find nearest neighbours which is time independent of the input data size. Points near depth discontinuations are also detected to reinforce object boundaries in the clustering process. The search method presented is evaluated using both indoor and outdoor dense depth images and demonstrates significant improvements in speed and precision compared to the commonly used Fast library for approximate nearest neighbour (FLANN) [Muja and Lowe, 2009].

Real-time mobile 3D temperature mapping

The ability to measure surface temperature and represent it on a metrically accurate 3D model has proven applications in many areas such as medical imaging, building energy auditing, and search and rescue. A system is proposed that enables this task to be performed with a handheld sensor, and for the first time with results able to be visualized and analyzed in real-time. A device comprising a thermal-infrared camera and range sensor is calibrated geometrically and used for data capture. The device is localized using a combination of ICP and video-based pose estimation from the thermal-infrared video footage which is shown to reduce the occurrence of failure modes. Furthermore, the problem of misregistration which can introduce severe distortions in assigned surface temperatures is avoided through the use of a risk-averse neighborhood weighting mechanism. Results demonstrate that the system is more stable and accurate than previous approaches, and can be used to accurately model complex objects and environments for practical tasks.

HeatWave : a handheld 3D thermography system for energy auditing

Energy auditing is an effective but costly approach for reducing the long-term energy consumption of buildings. When well-executed, energy loss can be quickly identified in the building structure and its subsystems. This then presents opportunities for improving energy efficiency. We present a low-cost, portable technology called "HeatWave" which allows non-experts to generate detailed 3D surface temperature models for energy auditing. This handheld 3D thermography system consists of two commercially available imaging sensors and a set of software algorithms which can be run on a laptop. The 3D model can be visualized in real-time by the operator so that they can monitor their degree of coverage as the sensors are used to capture data. In addition, results can be analyzed offline using the proposed "Spectra" multispectral visualization toolbox. The presence of surface temperature data in the generated 3D model enables the operator to easily identify and measure thermal irregularities such as thermal bridges, insulation leaks, moisture build-up and HVAC faults. Moreover, 3D models generated from subsequent audits of the same environment can be automatically compared to detect temporal changes in conditions and energy use over time.

A derivation of high-frequency asymptotic values of 3D added mass and damping based on properties of the Cummins' equation

This brief paper provides a novel derivation of the known asymptotic values of three-dimensional (3D) added mass and damping of marine structures in waves. The derivation is based on the properties of the convolution terms in the Cummins's Equation as derived by Ogilvie. The new derivation is simple and no approximations or series expansions are made. The results follow directly from the relative degree and low-frequency asymptotic properties of the rational representation of the convolution terms in the frequency domain. As an application, the extrapolation of damping values at high frequencies for the computation of retardation functions is also discussed.

Joint 2D and 3D cues for image segmentation towards robotic applications

This thesis investigates the fusion of 3D visual information with 2D image cues to provide 3D semantic maps of large-scale environments in which a robot traverses for robotic applications. A major theme of this thesis was to exploit the availability of 3D information acquired from robot sensors to improve upon 2D object classification alone. The proposed methods have been evaluated on several indoor and outdoor datasets collected from mobile robotic platforms including a quadcopter and ground vehicle covering several kilometres of urban roads.

Augmenting and assisting model elicitation tasks with 3D virtual world context metadata

Accurate process model elicitation continues to be a time consuming task, requiring skill on the part of the interviewer to extract explicit and tacit process information from the interviewee. Many errors occur in this elicitation stage that would be avoided by better activity recall, more consistent specification methods and greater engagement in the elicitation process by interviewees. Theories of situated cognition indicate that interactive 3D representations of real work environments engage and prime the cognitive state of the viewer. In this paper, our major contribution is to augment a previous process elicitation methodology with virtual world context metadata, drawn from a 3D simulation of the workplace. We present a conceptual and formal approach for representing this contextual metadata, integrated into a process similarity measure that provides hints for the business analyst to use in later modelling steps. Finally, we conclude with examples from two use cases to illustrate the potential abilities of this approach.