A method for three-dimensional reconstruction of a train accident scene using photographs

Railway accidents that usually cause numerous property and life losses occurred in recent years all around the world. In general, resources such as financial supports and incident rescue programs are required to minimize the losses after an accident. Due to lack of comprehensive information collected from accident sites, most railway emergency management departments face a predicament in setting up rescue schemes. To tackle the issue, realistic three-dimensional virtual accident scene reconstruction technology is developed, which provides and visualises supplementary materials and information about a train accident and can offer assistance to emergency crews when making decisions. We propose a photo-based three-dimensional reconstruction framework of vehicles for measuring the positions and poses of carriages involved in an accident. We implement and examine two case studies to validate this reconstruction method, which performs well in the assigned tasks

Photo-based automatic 3D reconstruction of train accident scenes

Railway accidents place significant demands on the resources of, and support from, railway emergency management departments. Once an accident occurs, an efficient incident rescue plan needs to be delivered as early as possible to minimise the loss of life and property. However, in the railway sector, most relevant departments currently face a challenge in drawing up a rescue scheme effectively and accurately with the insufficient information collected from the scene of a train accident. To assist with the rescue planning, we propose a framework which can rapidly and automatically construct a 3D virtual scene of a train accident by utilising photos of the accident spot. The framework uses a hybrid 3D reconstruction method to extract the position and pose information of the carriages involved in an accident. It adopts a geographic information system and a 3D visualisation engine to model and display the landscapes and buildings at the site of a train accident. In order to assess and validate our prototype, we quantitatively evaluate our main algorithm and demonstrate the usage of our technology with two case studies including a simulated scene with an in-lab setting and a real train derailment scene from on-site pictures. The results of both are accoun table with high accuracy and represent the ability of timely modelling and visualisation of a train accident scene

3D Body Shapes Estimation from Dressed-Human Silhouettes

Estimation of 3D body shapes from dressed-human photos is an important but challenging problem in virtual fitting. We propose a novel automatic framework to efficiently estimate 3D body shapes under clothes. We construct a database of 3D naked and dressed body pairs, based on which we learn how to predict 3D positions of body landmarks (which further constrain a parametric human body model) automatically according to dressed-human silhouettes. Critical vertices are selected on 3D registered human bodies as landmarks to represent body shapes, so as to avoid the time-consuming vertices correspondences finding process for parametric body reconstruction. Our method can estimate 3D body shapes from dressed-human silhouettes within 4 seconds, while the fastest method reported previously need 1 minute. In addition, our estimation error is within the size tolerance for clothing industry. We dress 6042 naked bodies with 3 sets of common clothes by physically based cloth simulation technique. To the best of our knowledge, We are the first to construct such a database containing 3D naked and dressed body pairs and our database may contribute to the areas of human body shapes estimation and cloth simulation

A Linear Approach for Depth and Colour Camera Calibration Using Hybrid Parameters

Many recent applications of computer graphics and human computer interaction have adopted both colour cameras and depth cameras as input devices. Therefore, an effective calibration of both types of hardware taking different colour and depth inputs is required. Our approach removes the numerical difficulties of using non-linear optimization in previous methods which explicitly resolve camera intrinsics as well as the transformation between depth and colour cameras. A matrix of hybrid parameters is introduced to linearize our optimization. The hybrid parameters offer a transformation from a depth parametric space (depth camera image) to a colour parametric space (colour camera image) by combining the intrinsic parameters of depth camera and a rotation transformation from depth camera to colour camera. Both the rotation transformation and intrinsic parameters can be explicitly calculated from our hybrid parameters with the help of a standard QR factorisation. We test our algorithm with both synthesized data and real-world data where ground-truth depth information is captured by Microsoft Kinect. The experiments show that our approach can provide comparable accuracy of calibration with the state-of-the-art algorithms while taking much less computation time (1/50 of Herrera’s method and 1/10 of Raposo’s method) due to the advantage of using hybrid parameters

Image-based 3D Scene Reconstruction and Rescue Simulation Framework for Railway Accidents

Although the railway transport is regarded as a relatively safe transportation tool, many railway accidents have still happened worldwide. In this research, an image-based 3D scene reconstruction framework was proposed to help railway accident emergency rescues. Based on the improved constrained non-linear least square optimization, the framework can automatically model the accident scene with only one panorama in a short time. We embedded the self-developed global terrain module into the commercial visualization and physics engine, which makes the commercial engine can be used to render the static scene at anywhere and simulate the dynamic rescue process respectively. In addition, a Head Mounted Device (HMD) was integrated into this framework to allow users to verify their rescue plan and review previous railway accidents in an immersive environment

A data-driven dynamics simulation framework for railway vehicles

The finite element (FE) method is essential for simulating vehicle dynamics with fine details, especially for train crash simulations. However, factors such as the complexity of meshes and the distortion involved in a large deformation would undermine its calculation efficiency. An alternative method, the multi-body (MB) dynamics simulation provides satisfying time efficiency but limited accuracy when highly nonlinear dynamic process is involved. To maintain the advantages of both methods, this paper proposes a data-driven simulation framework for dynamics simulation of railway vehicles. This framework uses machine learning techniques to extract nonlinear features from training data generated by FE simulations so that specific mesh structures can be formulated by a surrogate element (or surrogate elements) to replace the original mechanical elements, and the dynamics simulation can be implemented by co-simulation with the surrogate element(s) embedded into a MB model. This framework consists of a series of techniques including data collection, feature extraction, training data sampling, surrogate element building, and model evaluation and selection. To verify the feasibility of this framework, we present two case studies, a vertical dynamics simulation and a longitudinal dynamics simulation, based on co-simulation with MATLAB/Simulink and Simpack, and a further comparison with a popular data-driven model (the Kriging model) is provided. The simulation result shows that using the legendre polynomial regression model in building surrogate elements can largely cut down the simulation time without sacrifice in accuracy