Results 21 to 30 of about 6,190,166 (311)
AdaPoinTr: Diverse Point Cloud Completion With Adaptive Geometry-Aware Transformers [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 In this paper, we propose a Transformer encoder-decoder architecture, called PoinTr, which reformulates point cloud completion as a set-to-set translation problem and employs a geometry-aware block to model local geometric relationships explicitly.
Xumin Yu +4 more
semanticscholar +1 more source
Self-Supervised Pretraining of 3D Features on any Point-Cloud [PDF]
IEEE International Conference on Computer Vision, 2021 Pretraining on large labeled datasets is a prerequisite to achieve good performance in many computer vision tasks like image recognition, video understanding etc.
Zaiwei Zhang +3 more
semanticscholar +1 more source
SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer [PDF]
IEEE International Conference on Computer Vision, 2021 Point cloud completion aims to predict a complete shape in high accuracy from its partial observation. However, previous methods usually suffered from discrete nature of point cloud and unstructured prediction of points in local regions, which makes it ...
Peng Xiang +6 more
semanticscholar +1 more source
Sensors, 2022 As one of the best means of obtaining the geometry information of special shaped structures, point cloud data acquisition can be achieved by laser scanning or photogrammetry.
Xinming Pu +3 more
doaj +1 more source
A method of point cloud data block registration with considering distance from point to surface [PDF]
Bulletin of the Polish Academy of Sciences: Technical Sciences, 2022 The number of scanner stations used to acquire point cloud data is limited, resulting in poor data registration. As a result, a cloud point block registration approach was proposed that took into account the distance between the point and the surface ...
Yinju Lu, Mingyi Duan, Shuguang Dai
doaj +1 more source
Grad-PU: Arbitrary-Scale Point Cloud Upsampling via Gradient Descent with Learned Distance Functions [PDF]
Computer Vision and Pattern Recognition, 2023 Most existing point cloud upsampling methods have roughly three steps: feature extraction, feature expansion and 3D coordinate prediction. However, they usually suffer from two critical issues: (1) fixed upsampling rate after one-time training, since the
Yun He +4 more
semanticscholar +1 more source
Rotation-Invariant Transformer for Point Cloud Matching [PDF]
Computer Vision and Pattern Recognition, 2023 The intrinsic rotation invariance lies at the core of matching point clouds with handcrafted descriptors. However, it is widely despised by recent deep matchers that obtain the rotation invariance extrinsically via data augmentation. As the finite number
Hao Yu +6 more
semanticscholar +1 more source
COMPARISON OF UAV IMAGERY-DERIVED POINT CLOUD TO TERRESTRIAL LASER SCANNER POINT CLOUD [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019 A small unmanned aerial vehicle (UAV) with survey-grade GNSS positioning is used to produce a point cloud for topographic mapping and 3D reconstruction.
S. Peterson, J. Lopez, R. Munjy
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MPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template
Applied Sciences, 2021 With advancements in photoelectric technology and computer image processing technology, the visual measurement method based on point clouds is gradually being applied to the 3D measurement of large workpieces. Point cloud registration is a key step in 3D
Shijie Su +4 more
doaj +1 more source
Virtual Namesake Point Multi-Source Point Cloud Data Fusion Based on FPFH Feature Difference
Sensors, 2021 There are many sources of point cloud data, such as the point cloud model obtained after a bundle adjustment of aerial images, the point cloud acquired by scanning a vehicle-borne light detection and ranging (LiDAR), the point cloud acquired by ...
Li Zheng, Zhukun Li
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