Results 51 to 60 of about 6,190,166 (311)
VIPDA: A Visually Driven Point Cloud Denoising Algorithm Based on Anisotropic Point Cloud Filtering
Frontiers in Signal Processing, 2022 Point clouds (PCs) provide fundamental tools for digital representation of 3D surfaces, which have a growing interest in recent applications, such as e-health or autonomous means of transport.
Tiziana Cattai +3 more
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Visual Point Cloud Forecasting Enables Scalable Autonomous Driving [PDF]
Computer Vision and Pattern Recognition, 2023 In contrast to extensive studies on general vision, pretraining for scalable visual autonomous driving remains seldom explored. Visual autonomous driving applications require features encompassing semantics, 3D geometry, and temporal information ...
Zetong Yang +3 more
semanticscholar +1 more source
Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data [PDF]
IEEE International Conference on Computer Vision, 2019 Deep learning techniques for point cloud data have demonstrated great potentials in solving classical problems in 3D computer vision such as 3D object classification and segmentation. Several recent 3D object classification methods have reported state-of-
M. Uy +4 more
semanticscholar +1 more source
Open-Vocabulary Point-Cloud Object Detection without 3D Annotation [PDF]
Computer Vision and Pattern Recognition, 2023 The goal of open-vocabulary detection is to identify novel objects based on arbitrary textual descriptions. In this paper, we address open-vocabulary 3D point-cloud detection by a dividing-and-conquering strategy, which involves: 1) developing a point ...
Yuheng Lu +6 more
semanticscholar +1 more source
Real3D-AD: A Dataset of Point Cloud Anomaly Detection [PDF]
Neural Information Processing Systems, 2023 High-precision point cloud anomaly detection is the gold standard for identifying the defects of advancing machining and precision manufacturing. Despite some methodological advances in this area, the scarcity of datasets and the lack of a systematic ...
Jiaqi Liu +7 more
semanticscholar +1 more source
Classification of ALS Point Cloud with Improved Point Cloud Segmentation and Random Forests
Remote Sensing, 2017 This paper presents an automated and effective framework for classifying airborne laser scanning (ALS) point clouds. The framework is composed of four stages: (i) step-wise point cloud segmentation, (ii) feature extraction, (iii) Random Forests (RF ...
Huan Ni, Xiangguo Lin, Jixian Zhang
doaj +1 more source
Remote Sensing, 2020 Because of low accuracy and density of crop point clouds obtained by the Unmanned Aerial Vehicle (UAV)-borne Light Detection and Ranging (LiDAR) scanning system of UAV, an integrated navigation and positioning optimization method based on the grasshopper
Jian Chen +4 more
doaj +1 more source
Point cloud filtering on UAV based point cloud
Measurement, 2019 Abstract Nowadays, Unmanned Aerial Vehicles (UAVs) have been attracted wide attentions such as a new measurement equipment and mapping, which are capable of the high-resolution point cloud data collection. In addition, a massive point cloud data has brought about the data filtering and irregular data organization for the generation of digital terrain
Mustafa Zeybek, İsmail Şanlıoğlu
openaire +1 more source
Deep Closest Point: Learning Representations for Point Cloud Registration [PDF]
IEEE International Conference on Computer Vision, 2019 Point cloud registration is a key problem for computer vision applied to robotics, medical imaging, and other applications. This problem involves finding a rigid transformation from one point cloud into another so that they align. Iterative Closest Point
Yue Wang, J. Solomon
semanticscholar +1 more source
Fused Projection-Based Point Cloud Segmentation
Sensors, 2022 Semantic segmentation is used to enable a computer to understand its surrounding environment. In image processing, images are partitioned into segments for this purpose.
Maximilian Kellner +2 more
doaj +1 more source