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Segmentation of 3D Point Cloud Data Based on Supervoxel Technique

R.S. Rampriya, R. Suganya
2020 Procedia Computer Science  
In this paper, the bottomup 3D point cloud supervoxel technique is proposed for segmenting both outdoor and indoor scenes.  ...  objects (segments).  ...  The main objective of this paper is to develop a spatial technique for segmenting outdoor and indoor point cloud scene using a 3D object oriented point cloud segmentation approach.  ... 
doi:10.1016/j.procs.2020.04.045 fatcat:w5qdy4mkr5dm7m7ruzafim4xsi

Object Partitioning Using Local Convexity

Simon Christoph Stein, Markus Schoeler, Jeremie Papon, Florentin Worgotter
2014 2014 IEEE Conference on Computer Vision and Pattern Recognition  
As an alternative to this, we present a new, efficient learning-and model-free approach for the segmentation of 3D point clouds into object parts.  ...  The algorithm begins by decomposing the scene into an adjacency-graph of surface patches based on a voxel grid.  ...  Acknowledgments The research leading to these results has received funding from the European Community's Seventh Framework Programme FP7/2007-2013 (Specific Programme Cooperation, Theme 3, Information  ... 
doi:10.1109/cvpr.2014.46 dblp:conf/cvpr/SteinSPW14 fatcat:ofja24igtrazbgrvjnp6jcqzxa

Deep Learning Based Semantic Labelling of 3D Point Cloud in Visual SLAM

Xuxiang Qi, Shaowu Yang, Yuejin Yan
2018 IOP Conference Series: Materials Science and Engineering  
The dense point cloud is built by using a state-of-the-art RGB-D SLAM system. It is further segmented into meaningful clusters using a graph-based method.  ...  Finally, these semantic labels are projected to the point cloud clusters to achieve a 3D dense semantic map. The effectiveness of our method is validated on a popular public dataset.  ...  The result of supervoxel can be represented by an adjacency graph The scene segmentation can be framed as a graph partitioning problem.  ... 
doi:10.1088/1757-899x/428/1/012023 fatcat:rjxz64wlxng6jnrjg4stsngif4

INDOOR NAVIGATION FROM POINT CLOUDS: 3D MODELLING AND OBSTACLE DETECTION

L. Díaz-Vilariño, P. Boguslawski, K. Khoshelham, H. Lorenzo, L. Mahdjoubi
2016 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
and using these for readapting the routes according to the real state of the indoor depictured by the laser scanner.  ...  The value and originality of the approach consist on considering point clouds not only for reconstructing semantically-rich 3D indoor models, but also for detecting potential obstacles in the route planning  ...  Research of the second and fifth author is supported by a National Priority Research Program NPRP award (NPRP-06-1208-2-492) from the Qatar National Research Fund (a member of The Qatar Foundation).  ... 
doi:10.5194/isprsarchives-xli-b4-275-2016 fatcat:qnkqlbe6hjh4ln33g4drhluyhu

INDOOR NAVIGATION FROM POINT CLOUDS: 3D MODELLING AND OBSTACLE DETECTION

L. Díaz-Vilariño, P. Boguslawski, K. Khoshelham, H. Lorenzo, L. Mahdjoubi
2016 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
and using these for readapting the routes according to the real state of the indoor depictured by the laser scanner.  ...  The value and originality of the approach consist on considering point clouds not only for reconstructing semantically-rich 3D indoor models, but also for detecting potential obstacles in the route planning  ...  Research of the second and fifth author is supported by a National Priority Research Program NPRP award (NPRP-06-1208-2-492) from the Qatar National Research Fund (a member of The Qatar Foundation).  ... 
doi:10.5194/isprs-archives-xli-b4-275-2016 fatcat:aw7cl6lw5fhhhlao4aifwxm35e

PIECEWISE-PLANAR APPROXIMATION OF LARGE 3D DATA AS GRAPH-STRUCTURED OPTIMIZATION

S. Guinard, L. Landrieu, L. Caraffa, B. Vallet
2019 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
</strong> We introduce a new method for the piecewise-planar approximation of 3D data, including point clouds and meshes.  ...  We compare our results with a state-of-the-art region-growing-based segmentation method and show a significant improvement both in terms of approximation error and computation efficiency.</p>  ...  ACKNOWLEDGMENTS The authors would like to acknowledge the DGA for their financial support of this work and Jean-Pierre Papelard (ENSG) for providing the indoor LiDAR scan.  ... 
doi:10.5194/isprs-annals-iv-2-w5-365-2019 fatcat:22edxvezefc4pgaz6qmfdus43i

Shape Segmentation by Approximate Convexity Analysis

Oliver Van Kaick, Noa Fish, Yanir Kleiman, Shmuel Asafi, Daniel Cohen-OR
2014 ACM Transactions on Graphics  
Our method is designed to handle incomplete shapes, represented by point clouds.  ...  We show segmentation results on shapes acquired by a range scanner, and an analysis of the robustness of our method to missing regions.  ...  We thank Hui Huang for providing some of the point cloud models and Chen et al. for the segmentation benchmark.  ... 
doi:10.1145/2611811 fatcat:6klvafugezahxgddfz3odfalju

Developing a Semantic-Driven Hybrid Segmentation Method for Point Clouds of 3D Shapes

Xiaowen Yang, Xie Han, Qingde Li, Ligang He, Min Pang, Caiqin Jia
2020 IEEE Access  
This stage often leads to multiple over-segmentation convex regions, which are then remerged by a variational method established based on the narrow-band theory.  ...  In the first stage, a new feature of point cloud, called Local Concave-Convex Histogram, is introduced to first extract saddle regions complying with the semantic boundary feature.  ...  The method could be applied to the point clouds of both outdoor and indoor scenes, as well as a single object.  ... 
doi:10.1109/access.2020.2976847 fatcat:r7hj5uzusjdohgva7yxsoeyyom

An Effective 3D Shape Descriptor for Object Recognition with RGB-D Sensors

Zhong Liu, Changchen Zhao, Xingming Wu, Weihai Chen
2017 Sensors  
the point cloud.  ...  The 3D point cloud data are obtained using the RGB-D images covered by the mask image. For a detailed description of how the mask image and the point cloud data are obtained please refer to [10] .  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s17030451 pmid:28245553 pmcid:PMC5375737 fatcat:dvorrzsye5em7ltrmly4sn5z4u

Non-Destructive Measurement of Three-Dimensional Plants Based on Point Cloud

Yawei Wang, Yifei Chen
2020 Plants  
convex connected patches method to segment the leaves and stem from the plant model; extracting the feature boundary points from the leaf point cloud, and using the contour extraction algorithm to get  ...  The strategy includes three steps—firstly, preprocessing 3D point cloud data, completing the 3D plant registration through point cloud outlier filtering and surface smooth method; secondly, using the locally  ...  (a) Segementation plant's point cloud into small regions. (b) Small regions into larger objects by convexity-concavity relationship. Figure 8 . 8 Figure 8.  ... 
doi:10.3390/plants9050571 pmid:32365673 pmcid:PMC7285297 fatcat:zo6cv4o3xveklk545r4r2uulkq

Fast 3D point cloud segmentation using supervoxels with geometry and color for 3D scene understanding

Francesco Verdoja, Diego Thomas, Akihiro Sugimoto
2017 2017 IEEE International Conference on Multimedia and Expo (ICME)  
This paper presents a novel fast method for 3D colored point cloud segmentation. It starts with supervoxel partitioning of the cloud, i.e., an oversegmentation of the points in the cloud.  ...  Segmentation of 3D colored point clouds is a research field with renewed interest thanks to recent availability of inexpensive consumer RGB-D cameras and its importance as an unavoidable low-level step  ...  We can then cut the dendogram at the d-th level of the hierarchy to obtain d regions, where d is the desired number of objects.  ... 
doi:10.1109/icme.2017.8019382 dblp:conf/icmcs/VerdojaTS17 fatcat:sswtxsxn6bcojpisqqnqsqopny

3D indoor scene modeling from RGB-D data: a survey

Kang Chen, Yu-Kun Lai, Shi-Min Hu
2015 Computational Visual Media  
However, modeling indoor 3D scenes remains a challenging problem because of the complex structure of interior objects and poor quality of RGB-D data acquired by consumer-level sensors.  ...  With the popularity of consumer-level RGB-D cameras, there is a growing interest in digitizing real-world indoor 3D scenes.  ...  Starting from a seed region in the over-segmentation, the point cloud of an individual object is detected and separated from the background by iteratively adding regions which help to increase classification  ... 
doi:10.1007/s41095-015-0029-x fatcat:eh33gwvoune5hhadzjucry6rtm

Beyond Point Clouds: Scene Understanding by Reasoning Geometry and Physics

Bo Zheng, Yibiao Zhao, Joey C. Yu, Katsushi Ikeuchi, Song-Chun Zhu
2013 2013 IEEE Conference on Computer Vision and Pattern Recognition  
In this paper, we present an approach for scene understanding by reasoning physical stability of objects from point cloud.  ...  stable objects by optimizing the stability and the scene prior.  ...  Acknowledgment This work is supported by MURI ONR N00014-10-1-0933 and DARPA MSEE grant FA 8650-11-1-7149, USA; Next-generation Energies for Tohoku Recovery (NET) and SCOPE, Japan.  ... 
doi:10.1109/cvpr.2013.402 dblp:conf/cvpr/ZhengZYIZ13 fatcat:jzpbstlw3jcs3aapw7tyi23fqa

Multi-View Fusion-Based 3D Object Detection for Robot Indoor Scene Perception

Li Wang, Ruifeng Li, Jingwen Sun, Xingxing Liu, Lijun Zhao, Hock Soon Seah, Chee Kwang Quah, Budianto Tandianus
2019 Sensors  
For each view, the robot performs a 2D object semantic segmentation and obtains 3D object point clouds.  ...  Then, an unsupervised segmentation method called Locally Convex Connected Patches (LCCP) is utilized to segment the object accurately from the background.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s19194092 fatcat:csdqvvuomjasnja2ec6j2hwejm

UNSUPERVISED SEGMENTATION OF INDOOR 3D POINT CLOUD: APPLICATION TO OBJECT-BASED CLASSIFICATION

F. Poux, C. Mattes, L. Kobbelt
2020 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
This paper provides a fully unsupervised region growing segmentation approach for efficient clustering of massive 3D point clouds.  ...  It permits to automatically segment indoor point clouds with no prior knowledge at commercially viable performance and is the foundation for efficient indoor 3D modelling in cluttered point clouds.  ...  published by MDPI for sponsoring the publication of this paper.  ... 
doi:10.5194/isprs-archives-xliv-4-w1-2020-111-2020 fatcat:2c4njhl3n5dzlkk5g56a76sqbi
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