Multi-agent Animation Techniques for Traffic Simulation in Urban Environments

We describe an approach of using the multi-agent animation method for traffic simulations. The presented simulation system uses an individual-based agent behaviour model and discrete cell-based roadway configurations. The behaviour model defines the driving characteristics of each vehicle agent in a simulated traffic network, and the cell-based configurations allow real-time dynamic path planning and efficient traffic flow simulations. By incorporating the advantages of discrete cellular automation algorithms and the continuous model of fluid dynamics, our 3D virtual reality traffic simulation system achieves realistic and accurate simulations in virtual environments

A user study on quantisation thresholds of triangle meshes.

We present the results of a user study on estimating a quantisation threshold above which the quantised triangle mesh is perceived as indistinguishable from its unquantised original. The design of the experiment and the analysis of the results focus on the comparison between two different quantisation methods: rounding, in which all bits above the threshold are put to zero; and dithering, in which all bits above the threshold are randomised. The results show that dithered meshes require more bits per vertex coordinate in order to reach the indistinguishability threshold, and while the difference between the two methods is small, around one bit per vertex coordinate, it is nevertheless statistically significant

Recovering dense 3D point clouds from single endoscopic image

YesRecovering high-quality 3D point clouds from monocular endoscopic images is a challenging task. This paper proposes a novel deep learning-based computational framework for 3D point cloud reconstruction from single monocular endoscopic images. An unsupervised mono-depth learning network is used to generate depth information from monocular images. Given a single mono endoscopic image, the network is capable of depicting a depth map. The depth map is then used to recover a dense 3D point cloud. A generative Endo-AE network based on an auto-encoder is trained to repair defects of the dense point cloud by generating the best representation from the incomplete data. The performance of the proposed framework is evaluated against state-of-the-art learning-based methods. The results are also compared with non-learning based stereo 3D reconstruction algorithms. Our proposed methods outperform both the state-of-the-art learning-based and non-learning based methods for 3D point cloud reconstruction. The Endo-AE model for point cloud completion can generate high-quality, dense 3D endoscopic point clouds from incomplete point clouds with holes. Our framework is able to recover complete 3D point clouds with the missing rate of information up to 60%. Five large medical in-vivo databases of 3D point clouds of real endoscopic scenes have been generated and two synthetic 3D medical datasets are created. We have made these datasets publicly available for researchers free of charge. The proposed computational framework can produce high-quality and dense 3D point clouds from single mono-endoscopy images for augmented reality, virtual reality and other computer-mediated medical applications

New haptic syringe device for virtual angiography training

Angiography is an important minimally invasive diagnostic procedure in endovascular interventions. Effective training for the procedure is expensive, time consuming and resource demanding. Realistic simulation has become a viable solution to addressing such challenges. However, much of previous work has been focused on software issues. In this paper, we present a novel hardware system-an interactive syringe device with haptics as an add-on hardware component to 3D VR angiography training simulator. Connected to a realistic 3D computer simulation environment, the hardware component provides injection haptic feedback effects for medical training. First, we present the design of corresponding novel electronic units consisting of many design modules. Second, we describe a curve fitting method to estimate injection dosage and injection speed of the contrast media based on voltage variation between the potentiometer to increase the realism of the simulated training. A stepper motor control method is developed to imitate the coronary pressure for force feedback of syringe. Experimental results show that the validity and feasibility of the new haptic syringe device for achieving good diffusion effects of contrast media in the simulation system. A user study experiment with medical doctors to assess the efficacy and realism of proposed simulator shows good outcomes

Object Registration in Semi-cluttered and Partial-occluded Scenes for Augmented Reality

This paper proposes a stable and accurate object registration pipeline for markerless augmented reality applications. We present two novel algorithms for object recognition and matching to improve the registration accuracy from model to scene transformation via point cloud fusion. Whilst the first algorithm effectively deals with simple scenes with few object occlusions, the second algorithm handles cluttered scenes with partial occlusions for robust real-time object recognition and matching. The computational framework includes a locally supported Gaussian weight function to enable repeatable detection of 3D descriptors. We apply a bilateral filtering and outlier removal to preserve edges of point cloud and remove some interference points in order to increase matching accuracy. Extensive experiments have been carried to compare the proposed algorithms with four most used methods. Results show improved performance of the algorithms in terms of computational speed, camera tracking and object matching errors in semi-cluttered and partial-occluded scenes

SLAM-based Dense Surface Reconstruction in Monocular Minimally Invasive Surgery and its Application to Augmented Reality.

While Minimally Invasive Surgery (MIS) offers considerable benefits to patients, it also imposes big challenges on a surgeon's performance due to well-known issues and restrictions associated with the field of view (FOV), hand-eye misalignment and disorientation, as well as the lack of stereoscopic depth perception in monocular endoscopy. Augmented Reality (AR) technology can help to overcome these limitations by augmenting the real scene with annotations, labels, tumour measurements or even a 3D reconstruction of anatomy structures at the target surgical locations. However, previous research attempts of using AR technology in monocular MIS surgical scenes have been mainly focused on the information overlay without addressing correct spatial calibrations, which could lead to incorrect localization of annotations and labels, and inaccurate depth cues and tumour measurements. In this paper, we present a novel intra-operative dense surface reconstruction framework that is capable of providing geometry information from only monocular MIS videos for geometry-aware AR applications such as site measurements and depth cues. We address a number of compelling issues in augmenting a scene for a monocular MIS environment, such as drifting and inaccurate planar mapping. Methods A state-of-the-art Simultaneous Localization And Mapping (SLAM) algorithm used in robotics has been extended to deal with monocular MIS surgical scenes for reliable endoscopic camera tracking and salient point mapping. A robust global 3D surface reconstruction framework has been developed for building a dense surface using only unorganized sparse point clouds extracted from the SLAM. The 3D surface reconstruction framework employs the Moving Least Squares (MLS) smoothing algorithm and the Poisson surface reconstruction framework for real time processing of the point clouds data set. Finally, the 3D geometric information of the surgical scene allows better understanding and accurate placement AR augmentations based on a robust 3D calibration. Results We demonstrate the clinical relevance of our proposed system through two examples: a) measurement of the surface; b) depth cues in monocular endoscopy. The performance and accuracy evaluations of the proposed framework consist of two steps. First, we have created a computer-generated endoscopy simulation video to quantify the accuracy of the camera tracking by comparing the results of the video camera tracking with the recorded ground-truth camera trajectories. The accuracy of the surface reconstruction is assessed by evaluating the Root Mean Square Distance (RMSD) of surface vertices of the reconstructed mesh with that of the ground truth 3D models. An error of 1.24mm for the camera trajectories has been obtained and the RMSD for surface reconstruction is 2.54mm, which compare favourably with previous approaches. Second, \textit{in vivo} laparoscopic videos are used to examine the quality of accurate AR based annotation and measurement, and the creation of depth cues. These results show the potential promise of our geometry-aware AR technology to be used in MIS surgical scenes. Conclusions The results show that the new framework is robust and accurate in dealing with challenging situations such as the rapid endoscopy camera movements in monocular MIS scenes. Both camera tracking and surface reconstruction based on a sparse point cloud are effective and operated in real-time. This demonstrates the potential of our algorithm for accurate AR localization and depth augmentation with geometric cues and correct surface measurements in MIS with monocular endoscopes