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23rd Congress of the International-Society-for-Photogrammetry-and-Remote-Sensing (ISPRS)

作者： Chen, Yi-Chen Lin, Chao-Hung Natl Cheng Kung Univ Dept Geomat Tainan 701 Taiwan

With the development of Web 2.0 and cyber city modeling, an increasing number of 3D models have been available on web-based model-sharing platforms with many applications such as navigation, urban planning, and virtual reality. Based on the concept of data reuse, a 3D model retrieval system is proposed to retrieve building models similar to a user-specified query. The basic idea behind this system is to reuse these existing 3D building models instead of reconstruction from point clouds. To efficiently retrieve models, the models in databases are compactly encoded by using a shape descriptor generally. However, most of the geometric descriptors in related works are applied to polygonal models. In this study, the input query of the model retrieval system is a point cloud acquired by Light Detection and Ranging (LiDAR) systems because of the efficient scene scanning and spatial information collection. Using point clouds with sparse, noisy, and incomplete sampling as input queries is more difficult than that by using 3D models. Because that the building roof is more informative than other parts in the airborne LiDAR point cloud, an image-based approach is proposed to encode both point clouds from input queries and 3D models in databases. The main goal of data encoding is that the models in the database and input point clouds can be consistently encoded. Firstly, top-view depth images of buildings are generated to represent the geometry surface of a building roof. Secondly, geometric features are extracted from depth images based on height, edge and plane of building. Finally, descriptors can be extracted by spatial histograms and used in 3D model retrieval system. For data retrieval, the models are retrieved by matching the encoding coefficients of point clouds and building models. In experiments, a database including about 900,000 3D models collected from the Internet is used for evaluation of data retrieval. The results of the proposed method show a clear superiority

关键词： point cloud encoding Spatial Histogram 3D Model Retrieval Cyber City Modeling

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point cloud encoding for 3D Building Model Retrieval

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IEEE TRANSACTIONS ON MULTIMEDIA 2014年第2期16卷 337-345页

作者： Chen, Jyun-Yuan Lin, Chao-Hung Hsu, Po-Chi Chen, Chung-Hao Natl Cheng Kung Univ Dept Geomat Tainan 70101 Taiwan Old Dominion Univ Dept Elect & Comp Engn Norfolk VA 23529 USA

An increasing number of three-dimensional (3D) building models are being made available on Web-based model-sharing platforms. Motivated by the concept of data reuse, an encoding approach is proposed for 3D building model retrieval using point clouds acquired by airborne light detection and ranging (LiDAR) systems. To encode LiDAR point clouds with sparse, noisy, and incomplete sampling, we introduce a novel encoding scheme based on a set of low-frequency spherical harmonic basis functions. These functions provide compact representation and ease the encoding difficulty coming from inherent noises of point clouds. Additionally, a data filling and resampling technique is proposed to solve the aliasing problem caused by the sparse and incomplete sampling of point clouds. Qualitative and quantitative analyses of LiDAR data show a clear superiority of the proposed method over related methods. A cyber campus generated by retrieving 3D building models with airborne LiDAR point clouds demonstrates the feasibility of the proposed method.

关键词： Cyber city modeling point cloud encoding 3D model retrieval

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An Effective encoding Method Based on Local Information for 3D point cloud Classification

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IEEE ACCESS 2019年 7卷 39369-39377页

作者： Song, Yanan Gao, Liang Li, Xinyu Pan, Quan-Ke Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China

point cloud is a collection of many unordered points. Deep learning network encounters difficulties in utilizing the local information of point cloud because of its irregular format. This is not conducive to the network to identify the details of the object. Some strategies that design new network structures are used to capture the local information, but they make the networks become complicated. This paper proposes an effective method by encoding points into feature vectors. The local information is represented using neighborhood points that are searched by the k-nearest neighbor method. The neighborhood points are converted into feature elements based on the distance between these points and the encoded point. The feature vector of each point consists of its coordinates and the corresponding feature elements. A simple deep learning network is used to process these feature vectors. The proposed method is applied to ModelNet40 shape classification benchmark. The experimental results show that the classification accuracy of the simple deep learning network is improved by the proposed method.

关键词： Deep learning local information point cloud encoding 3D classification

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LiDAR-based place recognition for mobile robots in ground/water surface multiple scenes

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JOURNAL OF FIELD ROBOTICS 2024年第2期42卷 539-558页

作者： Yan, Yaxuan Zhang, Haiyang Zhao, Changming Liu, Xuan Fu, Siyuan Beijing Inst Technol Sch Opt & Photon Beijing Peoples R China Minist Educ Key Lab Photoelect Imaging Technol & Syst Beijing Peoples R China

LiDAR-based 3D place recognition is an essential component of simultaneous localization and mapping systems in multi-scene robotic applications. However, extracting discriminative and generalizable global descriptors of point clouds is still an open issue due to the insufficient use of the information contained in the LiDAR scans in existing approaches. In this paper, we propose a novel spatial-temporal point cloud encoding network for multiple scenes, dubbed STM-Net, to fully fuse the multi-view spatial information and temporal information of LiDAR point clouds. Specifically, we first develop a spatial feature encoding module consisting of the single-view transformer and multi-view transformer. The module learns the correlation both within a single view and between two views by utilizing the multi-layer range images generated by spherical projection and multi-layer bird's eye view images generated by top-down projection. Then in the temporal feature encoding module, we exploit the temporal transformer to mine the temporal information in the sequential point clouds, and a NetVLAD layer is applied to aggregate features and generate sub-descriptors. Furthermore, we use a GeM pooling layer to fuse more information along the time dimension for the final global descriptors. Extensive experiments conducted on unmanned ground/surface vehicles with different LiDAR configurations indicate that our method (1) achieves superior place recognition performance than state-of-the-art algorithms, (2) generalizes well to diverse sceneries, (3) is robust to viewpoint changes, (4) can operate in real-time, demonstrating the effectiveness and satisfactory capability of the proposed approach and highlighting its promising applications in multi-scene place recognition tasks.

关键词： 3D place recognition multiple scenes point cloud encoding spatial-temporal fusion

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MODEL RETRIEVAL BASED ON point cloud encoding OF AIRBORNE LIDAR

MODEL RETRIEVAL BASED ON POINT CLOUD ENCODING OF AIRBORNE LI...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Chen, Jyun-Yuan Hsu, Po-Chi Lin, Chao-Hung Natl Cheng Kung Univ Dept Geomat Tainan 70101 Taiwan

ISBN: (纸本)9781467311595

Many building models are available in the internet. Based on the concept of data reuse, we propose a system to retrieve models whose shapes are similar to that of point clouds. To consistently encode building models and point clouds, a novel encoding approach is introduced. Our approach can not only encode polygon models but also can handle unorganized, noisy, and incomplete point clouds. In addition, to ease of encoding problem suffering from incomplete data, we propose a resampling technique for point clouds. The experimental results show that our approach can solve the inherent problems of point clouds, i.e., unorganized, noisy and incomplete, and can encode the 3D shape consistently.

关键词： point cloud encoding Modeling Spherical Harmonic Function Retrieval

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MODEL RETRIEVAL BASED ON point cloud encoding OF AIRBORNE LIDAR

MODEL RETRIEVAL BASED ON POINT CLOUD ENCODING OF AIRBORNE LI...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Jyun-Yuan Chen Po-Chi Hsu Chao-Hung Lin Department of Geomatics National Cheng Kung University

ISBN: (纸本)9781467311601

关键词： point cloud encoding Modeling Spherical Harmonic Function Retrieval

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