Results 41 to 50 of about 7,018 (281)
PointGS: Bridging and fusing geometric and semantic space for 3D point cloud analysis [PDF]
, 2023 Directly processing 3D point cloud data becomes dominant in classification and segmentation tasks. Present mainstream point based methods usually focus on learning in either geometric space ( PointNet++) or semantic space ( DGCNN). Owing to the irregular
Hussain, Amir +5 more
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Design of Mandibular Angle Osteotomy Plane Based on Point Cloud Semantic Segmentation Algorithm
Shanghai Jiaotong Daxue xuebao, 2022 Mandibular angle osteotomy is a popular craniofacial plastic surgery in recent years. Usually, preoperative planning of mandibular angle osteotomy is completed by an experienced doctor, which is cumbersome and time-consuming.
LÜ Chaofan, YAN Yingjie, LIN Li, CHAI Gang, BAO Jinsong
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DEEP LEARNING FOR SEMANTIC SEGMENTATION OF 3D POINT CLOUD [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019 Abstract. Cultural Heritage is a testimony of past human activity, and, as such, its objects exhibit great variety in their nature, size and complexity; from small artefacts and museum items to cultural landscapes, from historical building and ancient monuments to city centers and archaeological sites.
E. S. Malinverni +6 more
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A TWO-STAGE APPROACH FOR RARE CLASS SEGMENTATION IN LARGE-SCALE URBAN POINT CLOUDS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 Although deep learning has greatly improved the semantic segmentation accuracy of point clouds, the segmentation of rare classes in large-scale urban scenes has not been targeted in available methods.
X. Zhang, R. Xue, R. Xue, U. Soergel
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Investigate Indistinguishable Points in Semantic Segmentation of 3D Point Cloud
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 This paper investigates the indistinguishable points (difficult to predict label) in semantic segmentation for large-scale 3D point clouds. The indistinguishable points consist of those located in complex boundary, points with similar local textures but different categories, and points in isolate small hard areas, which largely harm the performance of ...
Mingye Xu +3 more
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Open-world Semantic Segmentation for LIDAR Point Clouds
, 2022 Accepted by ECCV 2022.
Jun Cen +7 more
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Deep Semantic Segmentation of 3D Plant Point Clouds
, 2021 Plant phenotyping is an essential step in the plant breeding cycle, necessary to ensure food safety for a growing world population. Standard procedures for evaluating three-dimensional plant morphology and extracting relevant phenotypic characteristics are slow, costly, and in need of automation. Previous work towards automatic semantic segmentation of
Karoline Heiwolt +2 more
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Real-Time LiDAR Point Cloud Semantic Segmentation for Autonomous Driving [PDF]
, 2021 LiDAR has been widely used in autonomous driving systems to provide high-precision 3D geometric information about the vehicle’s surroundings for perception, localization, and path planning.
Xinming Huang, Xing Xie, Lin Bai
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GrowSP: Unsupervised Semantic Segmentation of 3D Point Clouds
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 CVPR 2023.
Zihui Zhang +3 more
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Clustering-TinyPointNet for fast large-scale point cloud semantic segmentation [PDF]
, 2023 Efficient processing of massive point cloud datasets is crucial for achieving fast semantic segmentation in various applications. While PointNet++ has demonstrated excellent performance in point cloud segmentation, its processing speed may not meet the ...
Kerry Walsh (9843221) +4 more
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