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Generating Unrestricted 3D Adversarial Point Clouds
CoRR, 2021 Utilizing 3D point cloud data has become an urgent need for the deployment of artificial intelligence in many areas like facial recognition and self-driving. However, deep learning for 3D point clouds is still vulnerable to adversarial attacks, e.g., iterative attacks, point transformation attacks, and generative attacks. These attacks need to restrict
Xuelong Dai +3 more
openaire +2 more sources
Multi-dimensional key generation of ICMetrics for cloud computing [PDF]
, 2015 Despite the rapid expansion and uptake of cloud based services, lack of trust in the provenance of such services represents a significant inhibiting factor in the further expansion of such service.
Howells, Gareth +3 more
core +1 more source
THE FEASIBILITY OF 3D POINT CLOUD GENERATION FROM SMARTPHONES [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016 This paper proposes a new technique for increasing the accuracy of direct geo-referenced image-based 3D point cloud generated from low-cost sensors in smartphones.
N. Alsubaie, N. El-Sheimy
doaj +1 more source
GENERATION OF 3-D LARGE-SCALE MAPS USING LIDAR POINT CLOUD DATA [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2023 3-Dimensional geospatial data is essential for creating and utilizing real-world visualizations for analyzing infrastructure design improvements. However, techniques exist such as Total Station, Global Positioning System (GPS), and Google Earth data ...
L. Dhruwa, P. K. Garg
doaj +1 more source
Point Cloud Generation from sUAS-Mounted iPhone Imagery: Performance Analysis [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014 The rapidly growing use of sUAS technology and fast sensor developments continuously inspire mapping professionals to experiment with low-cost airborne systems. Smartphones has all the sensors used in modern airborne surveying systems, including GPS, IMU,
A. D. Ladai, J. Miller
doaj +1 more source
Case Study: Improving the Quality of Dairy Cow Reconstruction with a Deep Learning-Based Framework
Sensors, 2022 Three-dimensional point cloud generation systems from scanning data of a moving camera provide extra information about an object in addition to color. They give access to various prospective study fields for researchers.
Changgwon Dang +9 more
doaj +1 more source
Discover 3D Point Cloud Processing with Python [PDF]
, 2020 Tutorial to simply set up your python environment, start processing and visualize 3D point cloud data.
Poux, Florent
core
Patch-Wise Point Cloud Generation: A Divide-and-Conquer Approach [PDF]
, 2023 A generative model for high-fidelity point clouds is of great importance in synthesizing 3d environments for applications such as autonomous driving and robotics.
Wen, Cheng +3 more
core +1 more source
A Realism Metric for Generated LiDAR Point Clouds
International Journal of Computer Vision, 2022 AbstractA considerable amount of research is concerned with the generation of realistic sensor data. LiDAR point clouds are generated by complex simulations or learned generative models. The generated data is usually exploited to enable or improve downstream perception algorithms.
Larissa T. Triess +3 more
openaire +3 more sources
3D diffusion based generation model for point cloud annotation and generation [PDF]
Concurrent with the rapid advancement of applications and 3D scanning sensors, the demand for 3D deep learning based technology and data has increased dramatically.
Li, Tingting
core +3 more sources