scholarly journals Parameter Measurement of Live Animals Based on the Mirror of Multiview Point Cloud

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jinsong Zhang ◽  
Yue Qin ◽  
Li Wang
Keyword(s):  
Point Cloud ◽  
Measurement Method ◽  
Large Scale ◽  
Selective Breeding ◽  
The Body ◽  
Parameter Measurement ◽  
Point Cloud Data ◽  
Local Data ◽  
Cloud Data ◽  
Body Parameters

Scale and standardization are essential to the prosperity of the breeding industry. During large-scale, standardized breeding, the selective breeding of good livestock breeds hinges on the accurate measurement of body parameters for live animals. However, the complex shooting environment brings several urgent problems, such as the missing of many local data in the point cloud and the difficulty in the automatic acquisition of body data. To solve these problems, this paper proposes a method for parameter measurement of live animals based on the mirror of multiview point cloud. Firstly, the acquisition and stitching principles were given for the multiview point cloud data on body parameters of live animals. Next, the authors presented a way to make up for the data missing areas in the point cloud. Finally, this paper acquires the body mirror data of live animals and scientifically calculates the body parameters. The proposed measurement method was proved effective through experiments.

scholarly journals Efficient Calculation Method for Tree Stem Traits from Large-Scale Point Clouds of Forest Stands

2021 ◽  
Vol 13 (13) ◽  
pp. 2476
Author(s):  
Hiroshi Masuda ◽  
Yuichiro Hiraoka ◽  
Kazuto Saito ◽  
Shinsuke Eto ◽  
Michinari Matsushita ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Random Access ◽  
Point Clouds ◽  
Point Cloud Data ◽  
Forest Stands ◽  
Cross Sectional ◽  
Cloud Data ◽  
Wireframe Model

With the use of terrestrial laser scanning (TLS) in forest stands, surveys are now equipped to obtain dense point cloud data. However, the data range, i.e., the number of points, often reaches the billions or even higher, exceeding random access memory (RAM) limits on common computers. Moreover, the processing time often also extends beyond acceptable processing lengths. Thus, in this paper, we present a new method of efficiently extracting stem traits from huge point cloud data obtained by TLS, without subdividing or downsampling the point clouds. In this method, each point cloud is converted into a wireframe model by connecting neighboring points on the same continuous surface, and three-dimensional points on stems are resampled as cross-sectional points of the wireframe model in an out-of-core manner. Since the data size of the section points is much smaller than the original point clouds, stem traits can be calculated from the section points on a common computer. With the study method, 1381 tree stems were calculated from 3.6 billion points in ~20 min on a common computer. To evaluate the accuracy of this method, eight targeted trees were cut down and sliced at 1-m intervals; actual stem traits were then compared to those calculated from point clouds. The experimental results showed that the efficiency and accuracy of the proposed method are sufficient for practical use in various fields, including forest management and forest research.

scholarly journals CLUSTERING OF WALL GEOMETRY FROM UNSTRUCTURED POINT CLOUDS

2019 ◽  
Vol XLII-2/W9 ◽  
pp. 101-108 ◽  
Author(s):  
M. Bassier ◽  
M. Vergauwen
Keyword(s):  
Point Cloud ◽  
Conditional Random Field ◽  
Initial Point ◽  
Point Clouds ◽  
The Body ◽  
Information Modeling ◽  
Point Cloud Data ◽  
Ongoing Research ◽  
Cloud Data ◽  
Wall Geometry

<p><strong>Abstract.</strong> The automated reconstruction of Building Information Modeling (BIM) objects from point cloud data is still ongoing research. A key aspect is retrieving the proper observations for each object. After segmenting and classifying the initial point cloud, the labeled segments should be clustered according to their respective objects. However, this procedure is challenging due to noise, occlusions and the associativity between different objects. This is especially important for wall geometry as it forms the basis for further BIM reconstruction.</p><p> In this work, a method is presented to automatically group wall segments derived from point clouds according to the proper walls of a building. More specifically, a Conditional Random Field is employed that evaluates the context of each observation in order to determine which wall it belongs too. The emphasis is on the clustering of highly associative walls as this topic currently is a gap in the body of knowledge. First a set of classified planar primitives is obtained using algorithms developed in prior work. Next, both local and contextual features are extracted based on the nearest neighbors and a number of seeds that are heuristically determined. The final wall clusters are then computed by decoding the graph and thus the most likely configuration of the observations. The experiments prove that the used method is a promising framework for wall clustering from unstructured point cloud data. Compared to a conventional region growing method, the proposed method significantly reduces the rate of false positives, resulting in better wall clusters. A key advantage of the proposed method is its capability of dealing with complex wall geometry in entire buildings opposed to the presented methods in current literature.</p>

Three-Dimensional Reconstruction of Single Input Image Based on Point Cloud

2021 ◽  
Vol 87 (7) ◽  
pp. 479-484
Author(s):  
Yu Hou ◽  
Ruifeng Zhai ◽  
Xueyan Li ◽  
Junfeng Song ◽  
Xuehan Ma ◽  
...  
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Three Dimensional ◽  
Data Sets ◽  
Point Cloud Data ◽  
Three Dimensional Reconstruction ◽  
Data Set ◽  
Cloud Data ◽  
Dimensional Reconstruction ◽  
Institute Of Technology

Three-dimensional reconstruction from a single image has excellent future prospects. The use of neural networks for three-dimensional reconstruction has achieved remarkable results. Most of the current point-cloud-based three-dimensional reconstruction networks are trained using nonreal data sets and do not have good generalizability. Based on the Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago ()data set of large-scale scenes, this article proposes a method for processing real data sets. The data set produced in this work can better train our network model and realize point cloud reconstruction based on a single picture of the real world. Finally, the constructed point cloud data correspond well to the corresponding three-dimensional shapes, and to a certain extent, the disadvantage of the uneven distribution of the point cloud data obtained by light detection and ranging scanning is overcome using the proposed method.

Robust stairway-detection and localization method for mobile robots using a graph-based model and competing initializations

2018 ◽  
Vol 37 (12) ◽  
pp. 1463-1483 ◽  
Author(s):  
Thomas Westfechtel ◽  
Kazunori Ohno ◽  
Bärbel Mertsching ◽  
Ryunosuke Hamada ◽  
Daniel Nickchen ◽  
...  
Keyword(s):  
Mobile Robots ◽  
Point Cloud ◽  
Large Scale ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Average Error ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Depth Sensors ◽  
Main Challenge

One of the major challenges for mobile robots in human-shaped environments is navigating stairways. This study presents a method for accurately detecting, localizing, and estimating the characteristics of stairways using point cloud data. The main challenge is the wide variety of different structures and shapes of stairways. This challenge is often aggravated by an unfavorable position of the sensor, which leaves large parts of the stairway occluded. This can be further aggravated by sparse point data. We overcome these difficulties by introducing a three-dimensional graph-based stairway-detection method combined with competing initializations. The stairway graph characterizes the general structural design of stairways in a generic way that can be used to describe a large variety of different stairways. By using multiple ways to initialize the graph, we can robustly detect stairways even if parts of the stairway are occluded. Furthermore, by letting the initializations compete against each other, we find the best initialization that accurately describes the measured stairway. The detection algorithm utilizes a plane-based approach. We also investigate different planar segmentation algorithms and experimentally compare them in an application-orientated manner. Our system accurately detects and estimates the stairway parameters with an average error of only [Formula: see text] for a variety of stairways including ascending, descending, and spiral stairways. Our method works robustly with different depth sensors for either small- or large-scale environments and for dense and sparse point cloud data. Despite this generality, our system’s accuracy is higher than most state-of-the-art stairway-detection methods.

Research on BSP Algorithm of Construction of Large-Scale 3D Point Model

2014 ◽  
Vol 513-517 ◽  
pp. 461-465
Author(s):  
Song Liu ◽  
Xiao Yao Xie
Keyword(s):  
Parallel Computing ◽  
Data Processing ◽  
Point Cloud ◽  
Large Scale ◽  
Point Cloud Data ◽  
Point Model ◽  
Cloud Data ◽  
Data Processing Method ◽  
Low Efficiency ◽  
Cloud Data Processing

For the construction of large-scale surface features 3D point model, a large number of point cloud data processing calculations is needed. Previous model construction calculation was treated non-parallel manner successively and mostly with one by one point cloud. This data processing method is complex, low efficiency and requires vast computing resource. Accordance with the BSP parallel computing ideas, we design a point cloud data modeling algorithm based on BSP and build a Hama parallel computing cluster consisted of ordinary PCs. The results indicate that, large-scale 3D point model BSP construction algorithm can improve the efficiency of modeling calculations and reduce computing resources requirements for processing construction computing.

scholarly journals Transfer Learning Based Semantic Segmentation for 3D Object Detection from Point Cloud

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3964
Author(s):  
Muhammad Imad ◽  
Oualid Doukhi ◽  
Deok-Jin Lee
Keyword(s):  
Object Detection ◽  
Transfer Learning ◽  
Point Cloud ◽  
Large Scale ◽  
Semantic Segmentation ◽  
Point Cloud Data ◽  
Learning Stage ◽  
3D Object ◽  
Cloud Data ◽  
3D Object Detection

Three-dimensional object detection utilizing LiDAR point cloud data is an indispensable part of autonomous driving perception systems. Point cloud-based 3D object detection has been a better replacement for higher accuracy than cameras during nighttime. However, most LiDAR-based 3D object methods work in a supervised manner, which means their state-of-the-art performance relies heavily on a large-scale and well-labeled dataset, while these annotated datasets could be expensive to obtain and only accessible in the limited scenario. Transfer learning is a promising approach to reduce the large-scale training datasets requirement, but existing transfer learning object detectors are primarily for 2D object detection rather than 3D. In this work, we utilize the 3D point cloud data more effectively by representing the birds-eye-view (BEV) scene and propose a transfer learning based point cloud semantic segmentation for 3D object detection. The proposed model minimizes the need for large-scale training datasets and consequently reduces the training time. First, a preprocessing stage filters the raw point cloud data to a BEV map within a specific field of view. Second, the transfer learning stage uses knowledge from the previously learned classification task (with more data for training) and generalizes the semantic segmentation-based 2D object detection task. Finally, 2D detection results from the BEV image have been back-projected into 3D in the postprocessing stage. We verify results on two datasets: the KITTI 3D object detection dataset and the Ouster LiDAR-64 dataset, thus demonstrating that the proposed method is highly competitive in terms of mean average precision (mAP up to 70%) while still running at more than 30 frames per second (FPS).

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