scholarly journals Ming and Qing Dynasty Official-Style Architecture Roof Types Classification Based on the 3D Point Cloud

2021 ◽  
Vol 10 (10) ◽  
pp. 650
Author(s):  
Youqiang Dong ◽  
Miaole Hou ◽  
Biao Xu ◽  
Yihao Li ◽  
Yuhang Ji
Keyword(s):  
Qing Dynasty ◽  
3D Modeling ◽  
Point Cloud ◽  
Semantic Network ◽  
Extraction Methods ◽  
3D Point Cloud ◽  
Matching Graph ◽  
Data Source ◽  
Attributed Relational Graph ◽  
Ming And Qing Dynasty

The Ming and Qing Dynasty type of official-style architecture roof can provide plenty of prior knowledge relating to the structure and size of these works of architecture, and plays an important role in the fields of 3D modeling, semantic recognition and culture inheriting. In this paper, we take the 3D point cloud as the data source, and an automatic classification method for the roof type of Ming and Qing Dynasty official-style architecture based on the hierarchical semantic network is illustrated. To classify the roofs into the correct categories, the characteristics of different roof types are analyzed and features including SoRs, DfFtR, DoPP and NoREs are first selected; subsequently, the corresponding feature extraction methods are proposed; thirdly, aiming at the structure of the ridges, a matching graph relying on the attributed relational graph of the ridges is given; based on the former work, a hierarchical semantic network is proposed and the thresholds are determined with the help of the construction rules of the Ming and Qing Dynasty official-style architecture. In order to fully verify the efficiency of our proposed method, various types of Ming and Qing Dynasty official-style architecture roof are identified, and the experimental results show that all structures are classified correctly.

The Algorithm of Obtaining 3D Point Cloud Data in Ground Fissure

2014 ◽  
Vol 556-562 ◽  
pp. 3575-3578
Author(s):  
Ming Xia Xu ◽  
Bin Li ◽  
Ming Xiang Feng ◽  
Yong Long Xu ◽  
Guan Hu Wang
Keyword(s):  
Point Cloud ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Ground Fissure ◽  
Cloud Data ◽  
Source Data ◽  
Data Source ◽  
Spatial Morphology ◽  
Point Data ◽  
Research Achievement

In this paper, we focus on the problem of how to obtain 3D cloud point data from different data source in ground fissure. We mainly analyzed the spatial morphology and the data characteristics of the ground fissure. The curve formula is constructed in engineering geological section and designed the algorithm of obtaining 3D point cloud data. This algorithm has been verified in Xi’an area and obtained satisfactory results. The research achievement provides a new method for using multi-source data on 3D modeling ground fissure.

3D Modeling Approach of Building Construction based on Point Cloud Data Using LiDAR

2019 ◽  
Author(s):  
Byeongjun Oh ◽  
Minju Kim ◽  
Chanwoo Lee ◽  
Hunhee Cho ◽  
Kyung-In Kang
Keyword(s):  
3D Modeling ◽  
Point Cloud ◽  
Building Construction ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Modeling Approach

scholarly journals Label3DMaize: toolkit for 3D point cloud data annotation of maize shoots

GigaScience ◽  
2021 ◽  
Vol 10 (5) ◽  
Author(s):  
Teng Miao ◽  
Weiliang Wen ◽  
Yinglun Li ◽  
Sheng Wu ◽  
Chao Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Training Dataset ◽  
Growth Stages ◽  
3D Point Cloud ◽  
Plant Structure ◽  
Data Annotation ◽  
Cloud Data ◽  
Point Cloud Segmentation ◽  
Different Growth Stages

Abstract Background The 3D point cloud is the most direct and effective data form for studying plant structure and morphology. In point cloud studies, the point cloud segmentation of individual plants to organs directly determines the accuracy of organ-level phenotype estimation and the reliability of the 3D plant reconstruction. However, highly accurate, automatic, and robust point cloud segmentation approaches for plants are unavailable. Thus, the high-throughput segmentation of many shoots is challenging. Although deep learning can feasibly solve this issue, software tools for 3D point cloud annotation to construct the training dataset are lacking. Results We propose a top-to-down point cloud segmentation algorithm using optimal transportation distance for maize shoots. We apply our point cloud annotation toolkit for maize shoots, Label3DMaize, to achieve semi-automatic point cloud segmentation and annotation of maize shoots at different growth stages, through a series of operations, including stem segmentation, coarse segmentation, fine segmentation, and sample-based segmentation. The toolkit takes ∼4–10 minutes to segment a maize shoot and consumes 10–20% of the total time if only coarse segmentation is required. Fine segmentation is more detailed than coarse segmentation, especially at the organ connection regions. The accuracy of coarse segmentation can reach 97.2% that of fine segmentation. Conclusion Label3DMaize integrates point cloud segmentation algorithms and manual interactive operations, realizing semi-automatic point cloud segmentation of maize shoots at different growth stages. The toolkit provides a practical data annotation tool for further online segmentation research based on deep learning and is expected to promote automatic point cloud processing of various plants.

scholarly journals UAV Based Estimation of Forest Leaf Area Index (LAI) through Oblique Photogrammetry

2021 ◽  
Vol 13 (4) ◽  
pp. 803
Author(s):  
Lingchen Lin ◽  
Kunyong Yu ◽  
Xiong Yao ◽  
Yangbo Deng ◽  
Zhenbang Hao ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Point Cloud ◽  
Forest Canopy ◽  
Estimation Method ◽  
Vertical Direction ◽  
Estimation Accuracy ◽  
3D Point Cloud ◽  
Area Index ◽  
Better Than

As a key canopy structure parameter, the estimation method of the Leaf Area Index (LAI) has always attracted attention. To explore a potential method to estimate forest LAI from 3D point cloud at low cost, we took photos from different angles of the drone and set five schemes (O (0°), T15 (15°), T30 (30°), OT15 (0° and 15°) and OT30 (0° and 30°)), which were used to reconstruct 3D point cloud of forest canopy based on photogrammetry. Subsequently, the LAI values and the leaf area distribution in the vertical direction derived from five schemes were calculated based on the voxelized model. Our results show that the serious lack of leaf area in the middle and lower layers determines that the LAI estimate of O is inaccurate. For oblique photogrammetry, schemes with 30° photos always provided better LAI estimates than schemes with 15° photos (T30 better than T15, OT30 better than OT15), mainly reflected in the lower part of the canopy, which is particularly obvious in low-LAI areas. The overall structure of the single-tilt angle scheme (T15, T30) was relatively complete, but the rough point cloud details could not reflect the actual situation of LAI well. Multi-angle schemes (OT15, OT30) provided excellent leaf area estimation (OT15: R2 = 0.8225, RMSE = 0.3334 m2/m2; OT30: R2 = 0.9119, RMSE = 0.1790 m2/m2). OT30 provided the best LAI estimation accuracy at a sub-voxel size of 0.09 m and the best checkpoint accuracy (OT30: RMSE [H] = 0.2917 m, RMSE [V] = 0.1797 m). The results highlight that coupling oblique photography and nadiral photography can be an effective solution to estimate forest LAI.

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