Results 21 to 30 of about 496,863 (282)
Point Cloud Adversarial Perturbation Generation for Adversarial Attacks
IEEE Access, 2023 In recent years, 3D model analysis has made a revolutionary development. Point cloud contains rich 3D object geometry information, which is an important 3D object data format widely used in many applications. However, the irregularity and disorder of the
Fengmei He +3 more
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3D Point Cloud Generation Based on Multi-Sensor Fusion
Applied Sciences, 2022 Traditional precise engineering surveys adopt manual static, discrete observation, which cannot meet the dynamic, continuous, high-precision and holographic fine measurements required for large-scale infrastructure construction, operation and maintenance,
Yulong Han +5 more
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EPiC-GAN: Equivariant point cloud generation for particle jets
SciPost Physics, 2023 With the vast data-collecting capabilities of current and future high-energy collider experiments, there is an increasing demand for computationally efficient simulations.
Erik Buhmann, Gregor Kasieczka, Jesse Thaler
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Floor-plan generation from noisy point clouds
Fifteenth International Conference on Machine Vision (ICMV 2022), 2023 This paper proposes a growing-based floor-plan generation method that creates the global layout of buildings from noisy point clouds obtained by a stereo camera. We introduce a PCA-based line-growing concept with a subsequent filtering step, which is able to robustly handle the high noise levels in input point clouds.
Liu, Xin +2 more
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GPU‐Accelerated LOD Generation for Point Clouds
Computer Graphics Forum, 2023 AbstractAbout: We introduce a GPU‐accelerated LOD construction process that creates a hybrid voxel‐point‐based variation of the widely used layered point cloud (LPC) structure for LOD rendering and streaming. The massive performance improvements provided by the GPU allow us to improve the quality of lower LODs via color filtering while still increasing
Schütz, Markus +3 more
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Generating 3D Adversarial Point Clouds [PDF]
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 Deep neural networks are known to be vulnerable to adversarial examples which are carefully crafted instances to cause the models to make wrong predictions. While adversarial examples for 2D images and CNNs have been extensively studied, less attention has been paid to 3D data such as point clouds.
Xiang, Chong, Qi, Charles R., Li, Bo
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Deep Auxiliary Learning for Point Cloud Generation
IEEE Access, 2020 Generation point cloud from single image is a classical problem in computer vision. The learning methods for this task often adopt local distance metrics as loss function, which means the generated points are not easy to meet the overall shape ...
Fei Hu +4 more
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Multi-set canonical correlation analysis for 3D abnormal gait behaviour recognition based on virtual sample generation [PDF]
, 2020 Small sample dataset and two-dimensional (2D) approach are challenges to vision-based abnormal gait behaviour recognition (AGBR). The lack of three-dimensional (3D) structure of the human body causes 2D based methods to be limited in abnormal gait ...
Luo, Jian, Tjahjadi, Tardi
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View-Agnostic Point Cloud Generation for Occlusion Reduction in Aerial Lidar
Remote Sensing, 2022 Occlusions are one of the leading causes of data degradation in lidar. The presence of occlusions reduces the overall aesthetic quality of a point cloud, creating a signature that is specific to that viewpoint and sensor modality.
Nina Singer, Vijayan K. Asari
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Deep Artificial Correspondence Generation for 3D Point Cloud Registration [PDF]
Jisuanji kexue, 2023 To address the challenging problems of point cloud registration in 3D reconstruction(e.g.,difficulty in finding corresponding points,etc.),this paper proposes a point cloud registration method based on cross-attention and artificial correspondence ...
BAI Zhengyao, XU Zhu, ZHANG Yihan
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