Generation of three dimensional photo-realistic models from Lidar and image data

Li-Chee-Ming, Julien; Gumerov, Damir; Ciobanu, T.; Armenakis, Costas

doi:10.1109/tic-sth.2009.5444457

Cited by 11 publications

(11 citation statements)

References 4 publications

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“…Terrestrial LIDAR data was also collected with a mobile mapping system. We used these laser scanning data to reconstruct 3D photorealistic prismatic building models, using an established processing pipeline [7], consisting of: (1) point cloud registration, (2) automatic tie point collection, (3) geo-referencing, (4) surface modeling and (5) texture mapping. We used the Iterative Closest Point (ICP) algorithm [8] for the automatic registration of 3D point clouds from multiple scans.…”

Section: A Automatic Generation Of 3d Photorealistic Prismatic Buildmentioning

confidence: 99%

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3DTown: The automatic urban awareness project

Corral-Soto

Tal

Wang

et al. 2012

2012 IEEE Virtual Reality (VR)

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Abstract-The 3DTown project is focused on the development of a distributed system for sensing, interpreting and visualizing the real-time dynamics of urban life within the 3D context of a city. At the heart of this technology lies a core of algorithms that automatically integrate 3D urban models with data from pan/tilt video cameras, environmental sensors and other real-time information sources. A key challenge is the three-dimensionalization of pedestrians and vehicles tracked in 2D camera video, which requires automatic real-time computation of camera pose relative to the 3D urban environment. In this paper we report preliminary results from a prototype system we call 3DTown, which is composed of discrete modules connected through pre-determined communication protocols. Currently, these modules consist of: 1) A 3D modeling module that allows for the efficient reconstruction of building models and integration with indoor architectural plans; 2) A GeoWeb server that indexes a 3D urban database to render perspective views of both outdoor and indoor environments from any requested vantage; 3) Sensor modules that receive and distribute real-time data; 4) Tracking modules that detect and track pedestrians and vehicles in urban spaces and access highways; 5) Camera pose modules that automatically estimate camera pose relative to the urban environment; 6) Threedimensionalization modules that receive information from the GeoWeb server, tracking and camera pose modules in order to back-project image tracks to geolocate pedestrians and vehicles within the 3D model; 7) An animation module that represents geo-located dynamic agents as sprites; and 8) A web-based visualization module that allows a user to explore the resulting dynamic 3D visualization in a number of interesting ways. To demonstrate our system we have used a blend of automatic and semi-automatic methods to construct a rich and accurate 3D model of a university campus, including both outdoor and indoor detail. The demonstration allows web-based 3D visualization of recorded patterns of pedestrian and vehicle traffic on streets and highways, estimations of vehicle speed, and real-time (live) visualization of pedestrian traffic and temperature data at a particular test site. Having demonstrated the system for hundreds of people, we report our informal observations on the user reaction, potential application areas and on the main challenges that must be addressed to bring the system closer to deployment.

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Section: A Automatic Generation Of 3d Photorealistic Prismatic Buildmentioning

confidence: 99%

“…From these images, planar regions and their edges were extracted using a region growing method described in [9]. Those 2D edges were then transformed back to 3D to construct 3D planar surface [7]. For texture mapping a photogrammetric collinearity condition based method was used [10].…”

Section: A Automatic Generation Of 3d Photorealistic Prismatic Buildmentioning

confidence: 99%

3DTown: The automatic urban awareness project

Corral-Soto

Tal

Wang

et al. 2012

2012 IEEE Virtual Reality (VR)

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“…In [23], a fast, automated, scalable generation method is proposed to generate the textured 3D models of indoor environments, where the regular shape of the wall and the ceiling have been taken into consideration. Similarly, the Manhattan world assumption is widely considered for the generation of the building structures [5,18,27].…”

Section: Introductionmentioning

confidence: 99%

Robot manipulator self-identification for surrounding obstacle detection

Wang

Yang

et al. 2016

Multimed Tools Appl

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Obstacle detection plays an important role for robot collision avoidance and motion planning. This paper focuses on the study of the collision prediction of a dual-arm robot based on a 3D point cloud. Firstly, a self-identification method is presented based on the over-segmentation approach and the forward kinematic model of the robot. Secondly, a simplified 3D model of the robot is generated using the segmented point cloud. Finally, a collision prediction algorithm is proposed to estimate the collision parameters in real-time. Experimental studies using the Kinect sensor and the Baxter robot have been performed to demonstrate the performance of the proposed algorithms.

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“…Moreover, texture mapped 3D models are obtained by mapping photographic images onto geometric models, which enables combined usage of lasers along with highresolution cameras (Zalama et al, 2011). (Li-Chee-Ming et al, 2009) created these models by using image data automatically with 3D point cloud registration; automatic target recognition used for geo-referencing; automatic plane detection algorithm used for surface modeling; and texture mapping.…”

Section: Introductionmentioning

confidence: 99%

Damage Detection on Structures Using Texture Mapped Laser Point Clouds

Guldur

Hajjar

2014

Structures Congress 2014

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Damage due to age or accumulated damage from hazards on existing structures poses a worldwide problem. In order to evaluate the current status of aging, deteriorating and damaged structures, it is vital to accurately assess the present conditions. It is possible to capture the in situ condition of structures by using laser scanners that create dense 3D point clouds. However, extracting meaningful information from point clouds automatically is challenging, and the current state-of-the-art requires a certain amount of user-interaction. Previously in our research, widely accepted point cloud processing steps were used to divide laser scanner data into meaningful object clusters, on which several damage detection methods were applied. Our current focus is on combining useful information extracted from laser scanner data with color information since novel laser scanners are capable of recording color data mapped on 3D point clouds. The color data provides additional information in the 4 th dimension, which makes it possible to detect damage types such as cracks, corrosion, and related surface defects that are generally difficult to detect by using only laser scanner data, and also helps to track volumetric changes on structures such as spalling. Even though crack and corrosion detection from images with varying resolution are extensively researched topics, damage detection by using laser scanners with and without images is a new research area that holds many opportunities for enhancing current practice in structural health monitoring. The aim of our research is to benefit from the best features of both laser scans and images, and combine them to create an automatic and effective damage detection method, which will complement the need for skilled labor during visual inspections of damaged structures and allow documenting related information automatically. This work is of interest to researchers who are focused on structural health monitoring and interested in the use of new laser scanners and damage detection algorithms for structures, as well as practitioners, who will benefit exploring new methods for improving their understanding of laser scanning technology and its capabilities for robust damage detection and documentation.

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Generation of three dimensional photo-realistic models from Lidar and image data

Cited by 11 publications

References 4 publications

3DTown: The automatic urban awareness project

3DTown: The automatic urban awareness project

Robot manipulator self-identification for surrounding obstacle detection

Damage Detection on Structures Using Texture Mapped Laser Point Clouds

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