A fast Reconstruction Method of 3D Object Point Cloud Based on Realsense D435

2020 ◽  
Author(s):  
Wenyuan Ma ◽  
Kehan Yang
Keyword(s):  
Point Cloud ◽  
Reconstruction Method ◽  
Object Point ◽  
3D Object ◽  
Fast Reconstruction

scholarly journals 3D-ReConstnet: A Single-View 3D-Object Point Cloud Reconstruction Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 83782-83790
Author(s):  
Bin Li ◽  
Yonghan Zhang ◽  
Bo Zhao ◽  
Hongyao Shao
Keyword(s):  
Point Cloud ◽  
Object Point ◽  
3D Object ◽  
Single View

scholarly journals Automatic Reconstruction of Multi-Level Indoor Spaces from Point Cloud and Trajectory

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3493
Author(s):  
Gahyeon Lim ◽  
Nakju Doh
Keyword(s):  
Point Cloud ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Reconstruction Method ◽  
Complex Environments ◽  
Indoor Space ◽  
Multi Level ◽  
Level Building ◽  
Indoor Spaces ◽  
Data Term

Remarkable progress in the development of modeling methods for indoor spaces has been made in recent years with a focus on the reconstruction of complex environments, such as multi-room and multi-level buildings. Existing methods represent indoor structure models as a combination of several sub-spaces, which are constructed by room segmentation or horizontal slicing approach that divide the multi-room or multi-level building environments into several segments. In this study, we propose an automatic reconstruction method of multi-level indoor spaces with unique models, including inter-room and inter-floor connections from point cloud and trajectory. We construct structural points from registered point cloud and extract piece-wise planar segments from the structural points. Then, a three-dimensional space decomposition is conducted and water-tight meshes are generated with energy minimization using graph cut algorithm. The data term of the energy function is expressed as a difference in visibility between each decomposed space and trajectory. The proposed method allows modeling of indoor spaces in complex environments, such as multi-room, room-less, and multi-level buildings. The performance of the proposed approach is evaluated for seven indoor space datasets.

Real-Time 3D object detection using improved convolutional neural network based on image-driven point cloud

2021 ◽  
Vol 14 ◽  
Author(s):  
Zhiyong Gao ◽  
Jianhong Xiang
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Point Cloud ◽  
Point Clouds ◽  
3D Point Cloud ◽  
3D Object ◽  
3D Object Detection ◽  
Instance Segmentation

Background: While detecting the object directly from the 3D point cloud, the natural 3D patterns and invariance of 3D data are often obscure. Objective: In this work, we aimed at studying the 3D object detection from discrete, disordered and sparse 3D point clouds. Methods: The CNN is composed of the frustum sequence module, 3D instance segmentation module S-NET, 3D point cloud transformation module T-NET, and 3D boundary box estimation module E-NET. The search space of the object is determined by the frustum sequence module. The instance segmentation of the point cloud is performed by the 3D instance segmentation module. The 3D coordinates of the object are confirmed by the transformation module and the 3D bounding box estimation module. Results: Evaluated on KITTI benchmark dataset, our method outperforms the state of the art by remarkable margins while having real-time capability. Conclusion: We achieve real-time 3D object detection by proposing an improved convolutional neural network (CNN) based on image-driven point clouds.

PointDCCNet: 3D Object Categorization Network using Point Cloud Decomposition

2021 ◽  
Author(s):  
Siddharth Katageri ◽  
Sameer Kulmi ◽  
Ramesh Ashok Tabib ◽  
Uma Mudenagudi
Keyword(s):  
Point Cloud ◽  
Object Categorization ◽  
3D Object

Structural mapping of rock walls imaged with a LiDAR mounted on an unmanned aircraft system

2019 ◽  
Vol 7 (1) ◽  
pp. 21-38 ◽  
Author(s):  
Connor McAnuff ◽  
Claire Samson ◽  
Dave Melanson ◽  
Christopher Polowick ◽  
Erin Bethell
Keyword(s):  
Point Cloud ◽  
Ground Level ◽  
Unmanned Aircraft ◽  
Reconstruction Method ◽  
Angle Of Incidence ◽  
Fracture Density ◽  
Mining Operations ◽  
Structural Mapping ◽  
Geological Information ◽  
Aircraft System

Structural mapping of rock walls to determine fracture orientation provides critical geological information in support of mining operations. A helicopter-style UAS (rotor diameter 2 m; take-off mass 35 kg; payload mass 11 kg) instrumented with a high-resolution LiDAR imaged a 75 m long and 10–15 m high series of four adjacent rock walls at the Canadian Wollastonite mine. A point cloud with a density of 484 point/m2 acquired at an angle of incidence of ∼41.7° from a flight altitude of 41.7 m above ground level was selected for structural mapping. The point cloud was first meshed using the Poisson surface reconstruction method and then remeshed to achieve an even element size distribution. Visualization of the remeshed Poisson mesh using a 360° hue–saturation–lightness colour wheel highlighted areas of higher fracture density, whereas visualization using a 180° colour wheel accentuated sliver-like geological features. Two joint sets were identified at 156/82 and 241/86 (strike/dip in degrees). A total of 18 virtual strike measurements and 13 virtual dip measurements were within 10% of manual compass measurements. This study demonstrated that the task of structural mapping of large rock walls can be automated by processing 3D images acquired with a LiDAR mounted on a UAS.

Research on Digital Modeling about Free-Form Surface with Holes Based on Coordinate Measuring Machine

2011 ◽  
Vol 287-290 ◽  
pp. 2805-2809
Author(s):  
Ming Yu Huang ◽  
Xiu Juan Wu ◽  
Zhong Shi Jia ◽  
Hong Jun Ni ◽  
Jing Jing Lv ◽  
...  
Keyword(s):  
Point Cloud ◽  
Coordinate Measuring Machine ◽  
Free Form ◽  
Model Reconstruction ◽  
Contact Measurement ◽  
Digital Modeling ◽  
Measuring Machine ◽  
Fast Reconstruction ◽  
Free Form Surface ◽  
Free Form Surfaces

Data acquisition and model reconstruction of free-form surfaces with holes were been studied, based on coordinate measuring machines. First, the structural process of the parts was analyzed, the method of combinate contact measurement with non-contact measurement were used to get point cloud; Then the point cloud were been preprocessed, feature curve extracted and solid modeled; Finally, the restructure model was been quality assessed and accuracy assessed. Using the measurement of combinated contact and non-contact can also meet both the precision requirement of key part and the fast reconstruction requirement of non-critical part, which has great significance on that part to fast and accurate reconstruction.

Automatic reconstruction method for large scene based on multi-site point cloud stitching

Measurement ◽  
2019 ◽  
Vol 131 ◽  
pp. 590-596 ◽  
Author(s):  
Haonan Xu ◽  
Lei Yu ◽  
Junyi Hou ◽  
Shumin Fei
Keyword(s):  
Point Cloud ◽  
Reconstruction Method

DSP-Net: Dense-to-Sparse Proposal Generation Approach for 3D Object Detection on Point Cloud

2021 ◽  
Author(s):  
Xinrui Yan ◽  
Yuhao Huang ◽  
Shitao Chen ◽  
Zhixiong Nan ◽  
Jingmin Xin ◽  
...  
Keyword(s):  
Object Detection ◽  
Point Cloud ◽  
3D Object ◽  
3D Object Detection
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