scholarly journals Object-level Segmentation of RGBD Data

2014 ◽  
Vol II-3 ◽  
pp. 73-78 ◽  
Author(s):  
H. Huang ◽  
H. Jiang ◽  
C. Brenner ◽  
H. Mayer
Keyword(s):  
Global Optimization ◽  
Solid Body ◽  
Point Cloud ◽  
3D Model ◽  
3D Point Cloud ◽  
Indoor Scenes ◽  
Novel Method ◽  
3D Information ◽  
Object Level ◽  
Additional Channel

We propose a novel method to segment Microsoft™Kinect data of indoor scenes with the emphasis on freeform objects. We use the full 3D information for the scene parsing and the segmentation of potential objects instead of treating the depth values as an additional channel of the 2D image. The raw RGBD image is first converted to a 3D point cloud with color. We then group the points into patches, which are derived from a 2D superpixel segmentation. With the assumption that every patch in the point cloud represents (a part of) the surface of an underlying solid body, a hypothetical quasi-3D model – the "synthetic volume primitive" (SVP) is constructed by extending the patch with a synthetic extrusion in 3D. The SVPs vote for a common object via intersection. By this means, a freeform object can be "assembled" from an unknown number of SVPs from arbitrary angles. Besides the intersection, two other criteria, i.e., coplanarity and color coherence, are integrated in the global optimization to improve the segmentation. Experiments demonstrate the potential of the proposed method.

Detection based object labeling of 3D point cloud for indoor scenes

2016 ◽  
Vol 174 ◽  
pp. 1101-1106 ◽  
Author(s):  
Wei Liu ◽  
Shaozi Li ◽  
Donglin Cao ◽  
Songzhi Su ◽  
Rongrong Ji
Keyword(s):  
Point Cloud ◽  
3D Point Cloud ◽  
Indoor Scenes

scholarly journals A FAST METHOD FOR MEASURING THE SIMILARITY BETWEEN 3D MODEL AND 3D POINT CLOUD

2016 ◽  
Vol XLI-B1 ◽  
pp. 725-728
Author(s):  
Zongliang Zhang ◽  
Jonathan Li ◽  
Xin Li ◽  
Yangbin Lin ◽  
Shanxin Zhang ◽  
...  
Keyword(s):  
Surface Area ◽  
Point Cloud ◽  
Laser Scanning ◽  
3D Model ◽  
Average Distance ◽  
Synthetic Data ◽  
Fast Method ◽  
3D Point Cloud ◽  
Partial Similarity ◽  
Distance Weighted

This paper proposes a fast method for measuring the partial Similarity between 3D Model and 3D point Cloud (SimMC). It is crucial to measure SimMC for many point cloud-related applications such as 3D object retrieval and inverse procedural modelling. In our proposed method, the surface area of model and the Distance from Model to point Cloud (DistMC) are exploited as measurements to calculate SimMC. Here, DistMC is defined as the weighted distance of the distances between points sampled from model and point cloud. Similarly, Distance from point Cloud to Model (DistCM) is defined as the average distance of the distances between points in point cloud and model. In order to reduce huge computational burdens brought by calculation of DistCM in some traditional methods, we define SimMC as the ratio of weighted surface area of model to DistMC. Compared to those traditional SimMC measuring methods that are only able to measure global similarity, our method is capable of measuring partial similarity by employing distance-weighted strategy. Moreover, our method is able to be faster than other partial similarity assessment methods. We demonstrate the superiority of our method both on synthetic data and laser scanning data.

scholarly journals Semantic Geometric Modelling of Unstructured Indoor Point Cloud

2018 ◽  
Vol 8 (1) ◽  
pp. 9 ◽  
Author(s):  
Wenzhong Shi ◽  
Wael Ahmed ◽  
Na Li ◽  
Wenzheng Fan ◽  
Haodong Xiang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Synthetic Data ◽  
Real Data ◽  
Wall Surface ◽  
Building Models ◽  
Model Complex ◽  
Indoor Scenes ◽  
Novel Method ◽  
First Time

A method capable of automatically reconstructing 3D building models with semantic information from the unstructured 3D point cloud of indoor scenes is presented in this paper. This method has three main steps: 3D segmentation using a new hybrid algorithm, room layout reconstruction, and wall-surface object reconstruction by using an enriched approach. Unlike existing methods, this method aims to detect, cluster, and model complex structures without having prior scanner or trajectory information. In addition, this method enables the accurate detection of wall-surface “defacements”, such as windows, doors, and virtual openings. In addition to the detection of wall-surface apertures, the detection of closed objects, such as doors, is also possible. Hence, for the first time, the whole 3D modelling process of the indoor scene from a backpack laser scanner (BLS) dataset was achieved and is recorded for the first time. This novel method was validated using both synthetic data and real data acquired by a developed BLS system for indoor scenes. Evaluating our approach on synthetic datasets achieved a precision of around 94% and a recall of around 97%, while for BLS datasets our approach achieved a precision of around 95% and a recall of around 89%. The results reveal this novel method to be robust and accurate for 3D indoor modelling.

A Novel Method of Cone Fitting Based on 3D Point Cloud

2014 ◽  
Vol 722 ◽  
pp. 321-326
Author(s):  
Jun Deng ◽  
Gui Hua Liu ◽  
Li Yue
Keyword(s):  
Objective Function ◽  
Point Cloud ◽  
Apex Angle ◽  
Conical Surface ◽  
Measuring System ◽  
3D Point Cloud ◽  
Cloud Data ◽  
Axis Direction ◽  
Self Development ◽  
Novel Method

The accuracy and stability of circular conical surface is easily influenced by the different distribution of noise and points in point cloud, when 3D point cloud data fits conical surface. For this reason, this paper proposes a novel method of establishing and optimizing the objective function with conical vertex. First, calculate the axis direction and half-apex angle of the cone; second, calculate conical vertex according to the result got in the first step; third, establish objective function with axis direction and half-apex angle and vertex, and optimize it with Levenberg-Marquardt. The method’s feasibility and robustness are verified by simulating and real experiments. The proposed algorithm has been successfully applied to a self-development 3D measuring system (TN 3DOMS) of Mianyang Saint Buffalo Technologies Limited Company.

scholarly journals 3D MODELING FOR ROCK ART DOCUMENTATION USING LIGHTWEIGHT MULTISPECTRAL CAMERA

2019 ◽  
Vol XLII-2/W9 ◽  
pp. 787-793 ◽  
Author(s):  
K. Zainuddin ◽  
Z. Majid ◽  
M. F. M. Ariff ◽  
K. M. Idris ◽  
M. A. Abbas ◽  
...  
Keyword(s):  
Point Cloud ◽  
3D Model ◽  
Rock Art ◽  
Three Dimensional ◽  
Laser Scanner ◽  
3D Point Cloud ◽  
Short Period ◽  
Dense Point ◽  
Close Range ◽  
Multispectral Camera

<p><strong>Abstract.</strong> This paper discusses the use of the lightweight multispectral camera to acquire three-dimensional data for rock art documentation application. The camera consists of five discrete bands, used for taking the motifs of the rock art paintings on a big structure of a cave based on the close-range photogrammetry technique. The captured images then processed using commercial structure-from-motion photogrammetry software, which automatically extracts the tie point. The extracted tie points were then used as input to generate a dense point cloud based on the multi-view stereo (MVS) and produced the multispectral 3D model, and orthophotos in a different wavelength. For comparison, the paintings and the wall surface also observed by using terrestrial laser scanner which capable of recording thousands of points in a short period of time with high accuracy. The cloud-to-cloud comparison between multispectral and TLS 3D point cloud show a sub-cm discrepancy, considering the used of the natural features as control target during 3D construction. Nevertheless, the processing also provides photorealistic orthophoto, indicates the advantages of the multispectral camera in generating dense 3D point cloud as TLS, photorealistic 3D model as RGB optic camera, and also with the multiwavelength output.</p>

scholarly journals GENERATION AND COMPARISON OF TLS AND SFM BASED 3D MODELS OF SOLID SHAPES IN HYDROMECHANIC RESEARCH

2016 ◽  
Vol XLI-B5 ◽  
pp. 925-929
Author(s):  
R. Zhang ◽  
D. Schneider ◽  
B. Strauß
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
3D Model ◽  
Accuracy Assessment ◽  
3D Models ◽  
3D Point Cloud ◽  
Synthetic Material ◽  
Qualitative Comparison ◽  
Software Packages ◽  
Main Influence

The aim of a current study at the Institute of Hydraulic Engineering and Technical Hydromechanics at TU Dresden is to develop a new injection method for quick and economic sealing of dikes or dike bodies, based on a new synthetic material. To validate the technique, an artificial part of a sand dike was built in an experimental hall. The synthetic material was injected, which afterwards spreads in the inside of the dike. After the material was fully solidified, the surrounding sand was removed with an excavator. In this paper, two methods, which applied terrestrial laser scanning (TLS) and structure from motion (SfM) respectively, for the acquisition of a 3D point cloud of the remaining shapes are described and compared. Combining with advanced software packages, a triangulated 3D model was generated and subsequently the volume of vertical sections of the shape were calculated. As the calculation of the volume revealed differences between the TLS and the SfM 3D model, a thorough qualitative comparison of the two models will be presented as well as a detailed accuracy assessment. The main influence of the accuracy is caused by generalisation in case of gaps due to occlusions in the 3D point cloud. Therefore, improvements for the data acquisition with TLS and SfM for such kind of objects are suggested in the paper.

3D Model Reconstruction System Development Based on Laser-Vision Technology

2016 ◽  
Vol 10 (5) ◽  
pp. 813-820 ◽  
Author(s):  
Huu-Cuong Nguyen ◽  
◽  
Byung-Ryong Lee
Keyword(s):  
Point Cloud ◽  
3D Model ◽  
Scanning System ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Model Reconstruction ◽  
Cloud Data ◽  
3D Model Reconstruction ◽  
Rotary Mechanism ◽  
Laser Vision

We propose a three-dimensional (3D) scanning system based on laser-vision technique and rotary mechanism combination for automatic 3D model reconstruction. The proposed scanning system consists of a laser projector, camera, and turntable. For laser-camera calibration, a new and simple method is applied. The 3D point cloud data of the surface of the scanned object are fully collected by integrating the extracted laser profiles from the laser stripe images corresponding to the rotary angles of the rotary mechanism. The obscured laser profile problem is solved by an additional camera at another viewpoint. From the collected 3D point cloud data, the 3D model of the scanned object is reconstructed based on the facet-representation method. The reconstructed 3D models showed the effectiveness and applicability of the proposed 3D scanning system in many 3D model-based applications.

scholarly journals GENERATION AND COMPARISON OF TLS AND SFM BASED 3D MODELS OF SOLID SHAPES IN HYDROMECHANIC RESEARCH

2016 ◽  
Vol XLI-B5 ◽  
pp. 925-929 ◽  
Author(s):  
R. Zhang ◽  
D. Schneider ◽  
B. Strauß
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
3D Model ◽  
Accuracy Assessment ◽  
3D Models ◽  
3D Point Cloud ◽  
Synthetic Material ◽  
Qualitative Comparison ◽  
Software Packages ◽  
Main Influence

The aim of a current study at the Institute of Hydraulic Engineering and Technical Hydromechanics at TU Dresden is to develop a new injection method for quick and economic sealing of dikes or dike bodies, based on a new synthetic material. To validate the technique, an artificial part of a sand dike was built in an experimental hall. The synthetic material was injected, which afterwards spreads in the inside of the dike. After the material was fully solidified, the surrounding sand was removed with an excavator. In this paper, two methods, which applied terrestrial laser scanning (TLS) and structure from motion (SfM) respectively, for the acquisition of a 3D point cloud of the remaining shapes are described and compared. Combining with advanced software packages, a triangulated 3D model was generated and subsequently the volume of vertical sections of the shape were calculated. As the calculation of the volume revealed differences between the TLS and the SfM 3D model, a thorough qualitative comparison of the two models will be presented as well as a detailed accuracy assessment. The main influence of the accuracy is caused by generalisation in case of gaps due to occlusions in the 3D point cloud. Therefore, improvements for the data acquisition with TLS and SfM for such kind of objects are suggested in the paper.

scholarly journals A FAST METHOD FOR MEASURING THE SIMILARITY BETWEEN 3D MODEL AND 3D POINT CLOUD

2016 ◽  
Vol XLI-B1 ◽  
pp. 725-728 ◽  
Author(s):  
Zongliang Zhang ◽  
Jonathan Li ◽  
Xin Li ◽  
Yangbin Lin ◽  
Shanxin Zhang ◽  
...  
Keyword(s):  
Surface Area ◽  
Point Cloud ◽  
Laser Scanning ◽  
3D Model ◽  
Average Distance ◽  
Synthetic Data ◽  
Fast Method ◽  
3D Point Cloud ◽  
Partial Similarity ◽  
Distance Weighted

This paper proposes a fast method for measuring the partial Similarity between 3D Model and 3D point Cloud (SimMC). It is crucial to measure SimMC for many point cloud-related applications such as 3D object retrieval and inverse procedural modelling. In our proposed method, the surface area of model and the Distance from Model to point Cloud (DistMC) are exploited as measurements to calculate SimMC. Here, DistMC is defined as the weighted distance of the distances between points sampled from model and point cloud. Similarly, Distance from point Cloud to Model (DistCM) is defined as the average distance of the distances between points in point cloud and model. In order to reduce huge computational burdens brought by calculation of DistCM in some traditional methods, we define SimMC as the ratio of weighted surface area of model to DistMC. Compared to those traditional SimMC measuring methods that are only able to measure global similarity, our method is capable of measuring partial similarity by employing distance-weighted strategy. Moreover, our method is able to be faster than other partial similarity assessment methods. We demonstrate the superiority of our method both on synthetic data and laser scanning data.

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