Results 21 to 30 of about 6,391,115 (351)

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection [PDF]

open access: yes2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017
Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality.
Yin Zhou, Oncel Tuzel
semanticscholar   +1 more source

Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis [PDF]

open access: yesIEEE International Conference on Computer Vision, 2021
Discrete point cloud objects lack sufficient shape descriptors of 3D geometries. In this paper, we present a novel method for aggregating hypothetical curves in point clouds.
Tiange Xiang   +4 more
semanticscholar   +1 more source

PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency [PDF]

open access: yesComputer Vision and Pattern Recognition, 2021
Removing outlier correspondences is one of the critical steps for successful feature-based point cloud registration. Despite the increasing popularity of introducing deep learning techniques in this field, spatial consistency, which is essentially ...
Xuyang Bai   +7 more
semanticscholar   +1 more source

Geodesics on Point Clouds [PDF]

open access: yesMathematical Problems in Engineering, 2014
We present a novel framework to compute geodesics on implicit surfaces and point clouds. Our framework consists of three parts, particle based approximate geodesics on implicit surfaces, Cartesian grid based approximate geodesics on point clouds, and geodesic correction.
Hongchuan Yu, Jian J. Zhang, Zheng Jiao
openaire   +2 more sources

Self-Supervised Pretraining of 3D Features on any Point-Cloud [PDF]

open access: yesIEEE 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

Picking Towels in Point Clouds [PDF]

open access: yesSensors, 2019
Picking clothing has always been a great challenge in laundry or textile industry automation, especially when some clothes are of the same colors, material and entangled with each other. In order to solve the problem, we present a grasp pose determination method to pick towels placed in a laundry basket or on a table. In our method, it is not needed to
Wang, Xiaoman   +5 more
openaire   +3 more sources

Topological Point Cloud Clustering

open access: yesProceedings of the 40th International Conference on Machine Learning. International Conference on Machine Learning, ICML 2023, Honolulu, USA, 2023
Accepted at the 40th International Conference on Machine Learning (ICML), 2023.
Grande, Vincent P., Schaub, Michael T.
openaire   +3 more sources

Point Cloud to Sound Cloud

open access: yesmagazén, 2022
The Space, Place, Sound, and Memory: Immersive Experiences of the Past project was led by dr James Cook, in collaboration with the Digital Documentation and Innovation team at Historic Environment Scotland, Soluis Heritage, the Binchois Consort, and ...
Cook, James, Mirashrafi, Sophia
doaj   +1 more source

PointMixup: Augmentation for Point Clouds [PDF]

open access: yes, 2020
This paper introduces data augmentation for point clouds by interpolation between examples. Data augmentation by interpolation has shown to be a simple and effective approach in the image domain. Such a mixup is however not directly transferable to point clouds, as we do not have a one-to-one correspondence between the points of two different objects ...
Chen, Y.   +6 more
openaire   +5 more sources

DPDist: Comparing Point Clouds Using Deep Point Cloud Distance [PDF]

open access: yes, 2020
We introduce a new deep learning method for point cloud comparison. Our approach, named Deep Point Cloud Distance (DPDist), measures the distance between the points in one cloud and the estimated surface from which the other point cloud is sampled. The surface is estimated locally and efficiently using the 3D modified Fisher vector representation.
Yizhak Ben-Shabat   +2 more
openaire   +4 more sources

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