scholarly journals Skeletonization via Local Separators

2021 ◽  
Vol 40 (5) ◽  
pp. 1-18
Author(s):  
Andreas Bærentzen ◽  
Eva Rotenberg
Keyword(s):  
Point Clouds ◽  
Polygonal Meshes ◽  
Shape Representations ◽  
Embedded Graphs ◽  
Geometric Data ◽  
Curve Skeleton

We propose a new algorithm for curve skeleton computation that differs from previous algorithms by being based on the notion of local separators . The main benefits of this approach are that it is able to capture relatively fine details and that it works robustly on a range of shape representations. Specifically, our method works on shape representations that can be construed as spatially embedded graphs. Such representations include meshes, volumetric shapes, and graphs computed from point clouds. We describe a simple pipeline where geometric data are initially converted to a graph, optionally simplified, local separators are computed and selected, and finally a skeleton is constructed. We test our pipeline on polygonal meshes, volumetric shapes, and point clouds. Finally, we compare our results to other methods for skeletonization according to performance and quality.

Mass-Driven Topology-Aware Curve Skeleton Extraction from Incomplete Point Clouds

2020 ◽  
Vol 26 (9) ◽  
pp. 2805-2817 ◽  
Author(s):  
Hongxing Qin ◽  
Jia Han ◽  
Ning Li ◽  
Hui Huang ◽  
Baoquan Chen
Keyword(s):  
Point Clouds ◽  
Skeleton Extraction ◽  
Curve Skeleton

scholarly journals Dynamic polygon clouds: representation and compression for VR/AR

2018 ◽  
Vol 7 ◽  
Author(s):  
Eduardo Pavez ◽  
Philip A. Chou ◽  
Ricardo L. de Queiroz ◽  
Antonio Ortega
Keyword(s):  
Point Cloud ◽  
State Of The Art ◽  
Three Dimensional ◽  
Point Clouds ◽  
Polygonal Meshes ◽  
Triangle Meshes ◽  
Previous State ◽  
Temporal Redundancy ◽  
Inter Frame ◽  
Robust To Noise

We introduce the polygon cloud, a compressible representation of three-dimensional geometry (including attributes, such as color), intermediate between polygonal meshes and point clouds. Dynamic polygon clouds, like dynamic polygonal meshes and dynamic point clouds, can take advantage of temporal redundancy for compression. In this paper, we propose methods for compressing both static and dynamic polygon clouds, specifically triangle clouds. We compare triangle clouds to both triangle meshes and point clouds in terms of compression, for live captured dynamic colored geometry. We find that triangle clouds can be compressed nearly as well as triangle meshes, while being more robust to noise and other structures typically found in live captures, which violate the assumption of a smooth surface manifold, such as lines, points, and ragged boundaries. We also find that triangle clouds can be used to compress point clouds with significantly better performance than previously demonstrated point cloud compression methods. For intra-frame coding of geometry, our method improves upon octree-based intra-frame coding by a factor of 5–10 in bit rate. Inter-frame coding improves this by another factor of 2–5. Overall, our proposed method improves over the previous state-of-the-art in dynamic point cloud compression by 33% or more.

Curve Skeleton Extraction From 3D Point Clouds Through Hybrid Feature Point Shifting and Clustering

2020 ◽  
Vol 39 (6) ◽  
pp. 111-132
Author(s):  
Hailong Hu ◽  
Zhong Li ◽  
Xiaogang Jin ◽  
Zhigang Deng ◽  
Minhong Chen ◽  
...  
Keyword(s):  
Point Clouds ◽  
Feature Point ◽  
Skeleton Extraction ◽  
3D Point Clouds ◽  
Curve Skeleton

scholarly journals HIGH DENSITY AERIAL IMAGE MATCHING: STATE-OF-THE-ART AND FUTURE PROSPECTS

2016 ◽  
Vol XLI-B4 ◽  
pp. 625-630 ◽  
Author(s):  
N. Haala ◽  
S. Cavegn
Keyword(s):  
Image Matching ◽  
State Of The Art ◽  
Point Clouds ◽  
High Density ◽  
Data Capture ◽  
Aerial Image ◽  
Special Focus ◽  
Second Phase ◽  
High Quality ◽  
Geometric Data

Ongoing innovations in matching algorithms are continuously improving the quality of geometric surface representations generated automatically from aerial images. This development motivated the launch of the joint ISPRS/EuroSDR project “Benchmark on High Density Aerial Image Matching”, which aims on the evaluation of photogrammetric 3D data capture in view of the current developments in dense multi-view stereo-image matching. Originally, the test aimed on image based DSM computation from conventional aerial image flights for different landuse and image block configurations. The second phase then put an additional focus on high quality, high resolution 3D geometric data capture in complex urban areas. This includes both the extension of the test scenario to oblique aerial image flights as well as the generation of filtered point clouds as additional output of the respective multi-view reconstruction. The paper uses the preliminary outcomes of the benchmark to demonstrate the state-of-the-art in airborne image matching with a special focus of high quality geometric data capture in urban scenarios.

scholarly journals HIGH DENSITY AERIAL IMAGE MATCHING: STATE-OF-THE-ART AND FUTURE PROSPECTS

2016 ◽  
Vol XLI-B4 ◽  
pp. 625-630 ◽  
Author(s):  
N. Haala ◽  
S. Cavegn
Keyword(s):  
Image Matching ◽  
State Of The Art ◽  
Point Clouds ◽  
High Density ◽  
Data Capture ◽  
Aerial Image ◽  
Special Focus ◽  
Second Phase ◽  
High Quality ◽  
Geometric Data

Ongoing innovations in matching algorithms are continuously improving the quality of geometric surface representations generated automatically from aerial images. This development motivated the launch of the joint ISPRS/EuroSDR project “Benchmark on High Density Aerial Image Matching”, which aims on the evaluation of photogrammetric 3D data capture in view of the current developments in dense multi-view stereo-image matching. Originally, the test aimed on image based DSM computation from conventional aerial image flights for different landuse and image block configurations. The second phase then put an additional focus on high quality, high resolution 3D geometric data capture in complex urban areas. This includes both the extension of the test scenario to oblique aerial image flights as well as the generation of filtered point clouds as additional output of the respective multi-view reconstruction. The paper uses the preliminary outcomes of the benchmark to demonstrate the state-of-the-art in airborne image matching with a special focus of high quality geometric data capture in urban scenarios.

Towards New Transitions Among Shape Representations

2003 ◽  
Author(s):  
Joris S. M. Vergeest ◽  
Chensheng Wang ◽  
Yu Song ◽  
Sander Spanjaard
Keyword(s):  
Feature Recognition ◽  
Shape Representation ◽  
Design Feature ◽  
Point Clouds ◽  
Shape Representations ◽  
New Methods ◽  
Feature Based ◽  
Representation Class ◽  
High Level ◽  
Feature Based Design

Four classes of shape representation are dominating nowadays in computer-supported design and modeling of products, (1) point clouds, (2) surface meshes, (3) solid/surface models and (4) design/styling models. To support applications such as high-level shape design, feature-based design, shape modeling, shape analysis, rapid prototyping, feature recognition and shape presentation, it is required that transitions among and within the four representation classes take place. Transitions from a “lower” representation class to “higher” class are far from trivial, and at the same time highly demanded for reverse design purposes. New methods and algorithms are needed to accomplish new transitions. A characterization of the four classes is presented, the most relevant transitions are reviewed and a relatively new transition, from point cloud directly to design/styling model is proposed and experimented. The importance of this transition for new methods of shape reuse and redesign is pointed out and demonstrated.

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

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