Scan-to-BIM for ‘secondary’ building components

Works dealing with Scan-to-BIM have, to date, principally focused on 'structural' components such as floors, ceilings and walls (with doors and windows). But the control of new facilities and the production of their corresponding as-is BIM models requires the identification and inspection of numerous other building components and objects, e.g. MEP components, such as plugs, switches, ducts, and signs. In this paper, we present a new 6D-based (XYZ + RGB) approach that processes dense coloured 3D points provided by terrestrial laser scanners in order to recognize the aforementioned smaller objects that are commonly located on walls. This paper focuses on the recognition of objects such as sockets, switches, signs, extinguishers and others. After segmenting the point clouds corresponding to the walls of a building, a set of candidate objects are detected independently in the colour and geometric spaces, and an original consensus procedure integrates both results in order to infer recognition. Finally, the recognized object is positioned and inserted in the as-is semantically-rich 3D model, or BIM model. The assessment of the method has been carried out in simulated scenarios under virtual scanning providing high recognition rates and precise positioning results. Experimental tests in real indoors using our MoPAD (Mobile Platform for Autonomous Digitization) platform have also yielded promising results.     

Procedural Modeling of a Building from a Single Image

Creating a virtual city is demanded for computer games, movies, and urban planning, but it takes a lot of time to create numerous 3D building models. Procedural modeling has become popular in recent years to overcome this issue, but creating a grammar to get a desired output is difficult and time consuming even for expert users. In this paper, we present an interactive tool that allows users to automatically generate such a grammar from a single image of a building. The user selects a photograph and highlights the silhouette of the target building as input to our method. Our pipeline automatically generates the building components, from large‐scale building mass to fine‐scale windows and doors geometry. Each stage of our pipeline combines convolutional neural networks (CNNs) and optimization to select and parameterize procedural grammars that reproduce the building elements of the picture. In the first stage, our method jointly estimates camera parameters and building mass shape. Once known, the building mass enables the rectification of the façades, which are given as input to the second stage that recovers the façade layout. This layout allows us to extract individual windows and doors that are subsequently fed to the last stage of the pipeline that selects procedural grammars for windows and doors. Finally, the grammars are combined to generate a complete procedural building as output. We devise a common methodology to make each stage of this pipeline tractable. This methodology consists in simplifying the input image to match the visual appearance of synthetic training data, and in using optimization to refine the parameters estimated by CNNs. We used our method to generate a variety of procedural models of buildings from existing photographs.     

Statistical segmentation and structural recognition for floor plan interpretation

A generic method for floor plan analysis and interpretation is presented in this article. The method, which is mainly inspired by the way engineers draw and interpret floor plans, applies two recognition steps in a bottom-up manner. First, basic building blocks, i.e., walls, doors, and windows are detected using a statistical patch-based segmentation approach. Second, a graph is generated, and structural pattern recognition techniques are applied to further locate the main entities, i.e., rooms of the building. The proposed approach is able to analyze any type of floor plan regardless of the notation used. We have evaluated our method on different publicly available datasets of real architectural floor plans with different notations. The overall detection and recognition accuracy is about 95 %, which is significantly better than any other state-of-the-art method. Our approach is generic enough such that it could be easily adopted to the recognition and interpretation of any other printed machine-generated structured documents.     

Mobile collaborative medical display system

Because of recent advances in wireless communication technologies, the world of mobile computing is flourishing with a variety of applications. In this study, we present an integrated architecture for a personal digital assistant (PDA)-based mobile medical display system that supports collaborative work between remote users. We aim to develop a system that enables users in different regions to share a working environment for collaborative visualization with the potential for exploring huge medical datasets. Our system consists of three major components: mobile client, gateway, and parallel rendering server. The mobile client serves as a front end and enables users to choose the visualization and control parameters interactively and cooperatively. The gateway handles requests and responses between mobile clients and the rendering server for efficient communication. Through the gateway, it is possible to share working environments between users, allowing them to work together in computer supported cooperative work (CSCW) mode. Finally, the parallel rendering server is responsible for performing heavy visualization tasks. Our experience indicates that some features currently available to our mobile clients for collaborative scientific visualization are limited due to the poor performance of mobile devices and the low bandwidth of wireless connections. However, as mobile devices and wireless network systems are experiencing considerable elevation in their capabilities, we believe that our methodology will be utilized effectively in building quite responsive, useful mobile collaborative medical systems in the very near future.     

基于Kinect v2的实时精确三维重建系统

快速、低成本、精确的三维扫描技术一直是计算机视觉领域研究的热点.首先,本文分析了新一代Kinect v2（Kinect for windows v2 sensor）的技术参数、测量原理.设计实验测得其深度精度与测量距离成线性变换关系.其次,Kinect v2深度数据含有大量的噪声尤其是在物体边缘,常用的双边滤波器等去噪算法不能很好的去除这些噪声,对此本文设计了一种有效的去噪算法,提高重建质量.最后,实现了一套基于新一代Kinect v2重建系统.实验结果表明,本文中的重建系统能够实时精确的重建物体,可以广泛应用于低成本的快速三维成型.     

基于RFID技术的绿色门窗监管及物流管理系统设计及实现

针对目前门窗市场管理不规范、监管手段缺乏的现象,同时考虑到门窗企业在物流管理方面的现实问题与需求,文章详细介绍了一种基于RFID技术的绿色门窗监管及物流管理系统。本系统具备绿色门窗产品入库管理、电子标签赋码管理、门窗信息查询和统计等功能,实现了绿色门窗信息的可追溯性,为行业管理部门提供可靠数据支持;同时,门窗生产企业可利用本系统对门窗物流及安装环节进行全过程监管,从而有效地减少企业经营成本、提高生产效率并提升行业信息化管理水平。     

室内平面图分块矢量化与高效三维建筑建模

针对建筑平面图的栅格图像,提出了一种全自动生成三维建筑模型的轻量计算方法。应用平均积分投影函数(integral projection function,IPF)方法对光栅图像进行区域分块,有效地抽取包含墙体对象的子区域。改进了基于非细化的稀疏点像素矢量化(sparse pixel vectorization,SPV)方法,用于抽取墙体的位置和尺寸等信息。为识别出墙体上的门窗和孔洞等建筑部件,将问题转化为图像多分类问题进行求解,同时设计了高效计算方案,精确地定位建筑部件在图纸中的位置。基于识别结果,进行三维建筑模型的快速生成,并集成于笔者所开发的三维建筑快速建模原型系统,方便了三维数据的加工和处理。通过大量实例,验证了所述方法的性能和效率。该方法可用于数字城市、虚拟现实内容创作和公共安全等领域。     

A body model server for human motion capture and representation

This paper presents a body model server (BMS) that provides real-time access to the position and posture of a person's torso, arms, hands, head, and eyes. It can be accessed by clients over a network. The BMS is designed to function as a device-independent data-layer between the sensing devices and client applications that require real-time human motion data, such as animation control. It can provide clients with accurate information at up to 40 Hz. For data collection, the model uses four magnetic position/orientation sensors, two data-gloves, and an eye-tracker. The BMS combines the data-streams from the sensors and transforms them into snapshots of the user's upper-body pose. A geometric model made up of joints and segments structures the input. Posture of the body is represented by joint angles. Two unique characteristics of our approach are the use of the implicit, geometric constraints of the sensed body to simplify the computation of the unmeasured joint angles, and the use of time-stamped data that allow synchronization with other data streams, e.g., speech input. This paper describes the architecture of the BMS, including the management of multiple input devices, the representation and computation of the position and joint angle data, and the client-server interface.     

Data Work: How Energy Advisors and Clients Make IoT Data Accountable

We present fieldwork findings from the deployment of an interactive sensing system that supports the work of energy advisors who give face-to-face advice to low-income households in the UK. We focus on how the system and the data it produced are articulated in the interactions between professional energy advisors and their clients, and how they collaboratively anticipate, rehearse, and perform data work. In addition to documenting how the system was appropriated in advisory work, we elaborate the ‘overhead cost’ of building collaborative action into connected devices and sensing systems, and the commensurate need to support discrete workflows and accountability systems to enable the methodical incorporation of the IoT into collaborative action. We contribute an elaboration of the social, collaborative methods of data work relevant to those who seek to design and study collaborative IoT systems.     

Web browsing on small-screen devices: a multiclient collaborative approach

A new multiclient collaborative system helps improve Web browsing on small-screen devices. Its two-level browsing scheme can comprehensively enhance mobile users' Web experiences. We propose a multiclient collaborative system that extends one device's display by uniting multiple displays on different clients. As our user study demonstrates, our system can overcome various small-screen Web browsing difficulties by offering a two-level browsing scheme-one for within-page browsing and the other for between-page comparative browsing.     

Detection of doors using a genetic visual fuzzy system for mobile robots

Doors are common objects in indoor environments and their detection can be used in robotic tasks such as map-building, navigation and positioning. This work presents a new approach to door-detection in indoor environments using computer vision. Doors are found in gray-level images by detecting the borders of their architraves. A variation of the Hough Transform is used in order to extract the segments in the image after applying the Canny edge detector. Features like length, direction, or distance between segments are used by a fuzzy system to analyze whether the relationship between them reveals the existence of doors. The system has been designed to detect rectangular doors typical of many indoor environments by the use of expert knowledge. Besides, a tuning mechanism based on a genetic algorithm is proposed to improve the performance of the system according to the particularities of the environment in which it is going to be employed. A large database of images containing doors of our building, seen from different angles and distances, has been created to test the performance of the system before and after the tuning process. The system has shown the ability to detect rectangular doors under heavy perspective deformations and it is fast enough to be used for real-time applications in a mobile robot.     

Designing peer-to-peer distributed user interfaces: Case studies on building distributed applications

Building a distributed user interface (DUI) application should ideally not require any additional effort beyond that necessary to build a non-distributed interface. In practice, however, DUI development is fraught with several technical challenges such as synchronization, resource management, and data transfer. In this paper, we present three case studies on building distributed user interface applications: a distributed media player for multiple displays and controls, a collaborative search system integrating a tabletop and mobile devices, and a multiplayer Tetris game for multi-surface use. While there exist several possible network architectures for such applications, our particular approach focuses on peer-to-peer (P2P) architectures. This focus leads to a number of challenges and opportunities. Drawing from these studies, we derive general challenges for P2P DUI development in terms of design, architecture, and implementation. We conclude with some general guidelines for practical DUI application development using peer-to-peer architectures.     

Dynamic energy-aware sensor configuration in multi-application monitoring systems

A typical pervasive monitoring system like a smart building depends on an infrastructure composed of hundreds of heterogeneous wireless sensor devices. Managing the energy consumption of these devices poses a challenging problem that affects the overall efficiency and usability. Existing approaches for sensor energy consumption typically assume a single monitoring application to consume sensor data and a static configuration for sensor devices. In this paper, we focus on a multi-application context with dynamic requirements and multi-modal sensor devices. We present 3SoSM, an approach to optimize interactions between application requirements and wireless sensor environment in real-time. It relies on an energy-aware dynamic configuration of sensor devices to lower energy consumption while fulfilling application requirements. To bind together sensor configuration and dynamic management of data streams, we design a sustainable multi-application monitoring system architecture for pervasive environments that collects application requirements for sensor data streams and optimizes them into sensor configurations. To demonstrate the effectiveness of our approach, a set of experiments are designed in the context of smart buildings. We comparatively evaluate our approach to show how dynamic sensor configuration for multiple monitoring applications indeed outperforms the mainstream duty-cycling method.     

论湘南(永州)地区门窗雕饰艺术的趣味变迁

隔扇门窗雕饰艺术在古建筑中是最富于变化的装饰部分;现代建筑与室内设计,由于追求外在形式,忽略了隔扇门窗内在的审美性与装饰趣味。以生活艺术化的审美观为出发点,从湘南(永州)地区传统隔扇门窗雕刻艺术的"形"、"意"、"神"等方面思考,使现代室内设计更好的继承传统雕饰艺术与传统元素。     

Automated 3D volumetric reconstruction of multiple-room building interiors for as-built BIM

Currently, fully automated as-built modeling of building interiors using point-cloud data still remains an open challenge, due to several problems that repeatedly arise: (1) complex indoor environments containing multiple rooms; (2) time-consuming and labor-intensive noise filtering; (3) difficulties of representation of volumetric and detail-rich objects such as windows and doors. This study aimed to overcome such limitations while improving the amount of details reproduced within the model for further utilization in BIM. First, we input just the registered three-dimensional (3D) point-cloud data and segmented the point cloud into separate rooms for more effective performance of the later modeling phases for each room. For noise filtering, an offset space from the ceiling height was used to determine whether the scan points belonged to clutter or architectural components. The filtered points were projected onto a binary map in order to trace the floor-wall boundary, which was further refined through subsequent segmentation and regularization procedures. Then, the wall volumes were estimated in two ways: inside- and outside-wall-component modeling. Finally, the wall points were segmented and projected onto an inverse binary map, thereby enabling detection and modeling of the hollow areas as windows or doors. The experimental results on two real-world data sets demonstrated, through comparison with manually-generated models, the effectiveness of our approach: the calculated RMSEs of the two resulting models were 0.089 m and 0.074 m, respectively.     

Multi-sensory and Multi-modal Fusion for Sentient Computing

This paper presents an approach to multi-sensory and multi-modal fusion in which computer vision information obtained from calibrated cameras is integrated with a large-scale sentient computing system known as “SPIRIT”. The SPIRIT system employs an ultrasonic location infrastructure to track people and devices in an office building and model their state. Vision techniques include background and object appearance modelling, face detection, segmentation, and tracking modules. Integration is achieved at the system level through the metaphor of shared perceptions, in the sense that the different modalities are guided by and provide updates to a shared world model. This model incorporates aspects of both the static (e.g. positions of office walls and doors) and the dynamic (e.g. location and appearance of devices and people) environment. Fusion and inference are performed by Bayesian networks that model the probabilistic dependencies and reliabilities of different sources of information over time. It is shown that the fusion process significantly enhances the capabilities and robustness of both sensory modalities, thus enabling the system to maintain a richer and more accurate world model.     

Automated residential building detection from airborne LiDAR data with deep neural networks

Detection of building objects in airborne LiDAR data is an essential task in many types of geospatial data applications such as urban reconstruction and damage assessment. Traditional approaches used in building detection often rely on shape primitives that can be detected by 2D/3D computer vision techniques. These approaches require carefully engineered features which tend to be specific to building types. Furthermore, these approaches are often computationally expensive with the increase of data size. In this paper, we propose a novel approach that employs a deep neural network to recognize and extract residential building objects in airborne LiDAR data. This proposed approach does not require any pre-defined geometric or texture features, and it is applicable to airborne LiDAR data sets with varied point densities and with damaged building objects. The latter makes our approach particularly useful in damage assessment applications. The research results show that the proposed approach is capable of achieving the state-of-the-art accuracy in building detection in these different types of point cloud data sets.     

Recursive non-rigid structure from motion with online learned shape prior

Most existing approaches in structure from motion for deformable objects focus on non-incremental solutions utilizing batch type algorithms. All data is collected before shape and motion reconstruction take place. This methodology is inherently unsuitable for applications that require real-time learning. Ideally the online system is capable of incrementally learning and building accurate shapes using current measurement data and past reconstructed shapes. Estimation of 3D structure and camera position is done online. To rely only on the measurements up until that moment is still a challenging problem.     

Personness estimation for real-time human detection on mobile devices

One aim of detection proposal methods is to reduce the computational overhead of object detection. However, most of the existing methods have significant computational overhead for real-time detection on mobile devices. A fast and accurate proposal method of human detection called personness estimation is proposed, which facilitates real-time human detection on mobile devices and can be effectively integrated into part-based detection, achieving high detection performance at a low computational cost. Our work is based on two observations: (i) normed gradients, which are designed for generic objectness estimation, effectively generate high-quality detection proposals for the person category; (ii) fusing the normed gradients with color attributes improves the performance of proposal generation for human detection. Thus, the candidate windows generated by the personness estimation will very likely contain human subjects. The human detection is then guided by the candidate windows, offering high detection performance even when the detection task terminates prior to completion. This interruptible detection scheme, called anytime detection, enables real-time human detection on mobile devices. Furthermore, we introduce a new evaluation methodology called time-recall curves to practically evaluate our approach. The applicability of our proposed method is demonstrated in extensive experiments on a publicly available dataset and a real mobile device, facilitating acquisition and enhancement of portrait photographs (e.g. selfie) on widespread mobile platforms.     

EasyHome: an online virtual home decoration system

This paper provides an online virtual 7home decoration system, which integrates web interaction technique into virtual home decoration. The main functions of this system include house design, house decoration, and data management. For the house design, an automatic joint method of the walls is proposed, in order to design various house. For the house decoration, we introduce the wall decoration principle corresponding to the algorithm of discretization on the walls, which contains doors or windows, and triangularization on the irregular floors. To variety of different textures and collage regulations, we provide a new texture collage mapping method. Technology of rendering to texture is used to generate a small geometry texture, which will be mapped into a large target object on the basis of the texture's structural properties. As a result, the whole texture continuity is also maintained. In data management aspect, we embed a browser into this system so that users can conveniently obtain decoration models on the basis of web interaction. After these models have been downloaded, they can be shown in virtual scene according to guide information. In addition, some quotation lists can be generated automatically. Experimental results show that an authentic house can be easily presented in our system. Copyright © 2013 John Wiley & Sons, Ltd.