Deep point embedding for urban classification using ALS point clouds: A new perspective from local to global

2020 ◽  
Vol 163 ◽  
pp. 62-81 ◽  
Author(s):  
Rong Huang ◽  
Yusheng Xu ◽  
Danfeng Hong ◽  
Wei Yao ◽  
Pedram Ghamisi ◽  
...  
Keyword(s):  
Point Clouds ◽  
Urban Classification ◽  
Deep Point ◽  
New Perspective

scholarly journals Sequential 3D Human Pose Estimation Using Adaptive Point Cloud Sampling Strategy

2021 ◽  
Author(s):  
Zihao Zhang ◽  
Lei Hu ◽  
Xiaoming Deng ◽  
Shihong Xia
Keyword(s):  
Pose Estimation ◽  
Point Cloud ◽  
Fundamental Problem ◽  
Estimation Method ◽  
Sampling Strategy ◽  
Point Clouds ◽  
Human Pose Estimation ◽  
Human Pose ◽  
New Perspective ◽  
3D Human Pose Estimation

3D human pose estimation is a fundamental problem in artificial intelligence, and it has wide applications in AR/VR, HCI and robotics. However, human pose estimation from point clouds still suffers from noisy points and estimated jittery artifacts because of handcrafted-based point cloud sampling and single-frame-based estimation strategies. In this paper, we present a new perspective on the 3D human pose estimation method from point cloud sequences. To sample effective point clouds from input, we design a differentiable point cloud sampling method built on density-guided attention mechanism. To avoid the jitter caused by previous 3D human pose estimation problems, we adopt temporal information to obtain more stable results. Experiments on the ITOP dataset and the NTU-RGBD dataset demonstrate that all of our contributed components are effective, and our method can achieve state-of-the-art performance.

scholarly journals CONTEXTUAL CLASSIFICATION OF POINT CLOUDS USING A TWO-STAGE CRF

2015 ◽  
Vol XL-3/W2 ◽  
pp. 141-148 ◽  
Author(s):  
J. Niemeyer ◽  
F. Rottensteiner ◽  
U. Soergel ◽  
C. Heipke
Keyword(s):  
Urban Areas ◽  
Clustering Algorithm ◽  
Conditional Random Field ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Two Stage ◽  
Urban Classification ◽  
Long Range Interactions ◽  
Set Up

In this investigation, we address the task of airborne LiDAR point cloud labelling for urban areas by presenting a contextual classification methodology based on a Conditional Random Field (CRF). A two-stage CRF is set up: in a first step, a point-based CRF is applied. The resulting labellings are then used to generate a segmentation of the classified points using a Conditional Euclidean Clustering algorithm. This algorithm combines neighbouring points with the same object label into one segment. The second step comprises the classification of these segments, again with a CRF. As the number of the segments is much smaller than the number of points, it is computationally feasible to integrate long range interactions into this framework. Additionally, two different types of interactions are introduced: one for the local neighbourhood and another one operating on a coarser scale. <br><br> This paper presents the entire processing chain. We show preliminary results achieved using the Vaihingen LiDAR dataset from the ISPRS Benchmark on Urban Classification and 3D Reconstruction, which consists of three test areas characterised by different and challenging conditions. The utilised classification features are described, and the advantages and remaining problems of our approach are discussed. We also compare our results to those generated by a point-based classification and show that a slight improvement is obtained with this first implementation.

scholarly journals 3D BUILDING RECONSTRUCTION FROM LIDAR POINT CLOUDS BY ADAPTIVE DUAL CONTOURING

2015 ◽  
Vol II-3/W4 ◽  
pp. 157-164 ◽  
Author(s):  
E. Orthuber ◽  
J. Avbelj
Keyword(s):  
Evaluation Method ◽  
Grid Cell ◽  
Region Growing ◽  
Point Clouds ◽  
Data Sets ◽  
Building Models ◽  
Urban Classification ◽  
Dual Contouring ◽  
Adaptive Thresholds ◽  
Building Reconstruction

This paper presents a novel workflow for data-driven building reconstruction from Light Detection and Ranging (LiDAR) point clouds. The method comprises building extraction, a detailed roof segmentation using region growing with adaptive thresholds, segment boundary creation, and a structural 3D building reconstruction approach using adaptive 2.5D Dual Contouring. First, a 2D-grid is overlain on the segmented point cloud. Second, in each grid cell 3D vertices of the building model are estimated from the corresponding LiDAR points. Then, the number of 3D vertices is reduced in a quad-tree collapsing procedure, and the remaining vertices are connected according to their adjacency in the grid. Roof segments are represented by a Triangular Irregular Network (TIN) and are connected to each other by common vertices or - at height discrepancies - by vertical walls. Resulting 3D building models show a very high accuracy and level of detail, including roof superstructures such as dormers. The workflow is tested and evaluated for two data sets, using the evaluation method and test data of the “ISPRS Test Project on Urban Classification and 3D Building Reconstruction” (Rottensteiner et al., 2012). Results show that the proposed method is comparable with the state of the art approaches, and outperforms them regarding undersegmentation and completeness of the scene reconstruction.

SREM observation of the interaction between the in-situ deposited Pd small particles and MgO cleavage surface

1986 ◽  
Vol 44 ◽  
pp. 382-383
Author(s):  
H.-J. Ou
Keyword(s):  
Electron Microscopy ◽  
Small Particle ◽  
Small Particles ◽  
Cleavage Surface ◽  
Metallic Particles ◽  
Microscopy Technique ◽  
Ceramic Surfaces ◽  
Reflection Electron Microscopy ◽  
New Perspective

The understanding of the interactions between the small metallic particles and ceramic surfaces has been studied by many catalyst scientists. We had developed Scanning Reflection Electron Microscopy technique to study surface structure of MgO hulk cleaved surface and the interaction with the small particle of metals. Resolutions of 10Å has shown the periodic array of surface atomic steps on MgO. The SREM observation of the interaction between the metallic particles and the surface may provide a new perspective on such processes.

Articulation Training: A New Perspective

1979 ◽  
Vol 10 (3) ◽  
pp. 145-151 ◽  
Author(s):  
Sallie W. Hillard ◽  
Laura P. Goepfert
Keyword(s):  
Life Experience ◽  
Remediation Measures ◽  
New Perspective

This paper describes the concept of teaching articulation through words which have inherent meaning to a child’s life experience, such as a semantically potent word approach. The approach was used with six children. Comparison of pre/post remediation measures indicated that it has promise as a technique for facilitating increased correct phoneme production.

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