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pro vyhledávání: '"Javaheri, Alireza"'
Autor:
Anaraki, Neda Rahimpour, Azadbakht, Alireza, Tahmasbi, Maryam, Farahani, Hadi, Kheradpisheh, Saeed Reza, Javaheri, Alireza
Recently, there has been a high demand for accelerating and improving the detection of automatic cadastral mapping. As this problem is in its starting point, there are many methods of computer vision and deep learning that have not been considered ye
Externí odkaz:
http://arxiv.org/abs/2309.16708
The human brain constantly learns and rapidly adapts to new situations by integrating acquired knowledge and experiences into memory. Developing this capability in machine learning models is considered an important goal of AI research since deep neur
Externí odkaz:
http://arxiv.org/abs/2306.04410
We propose a novel keypoint voting 6DoF object pose estimation method, which takes pure unordered point cloud geometry as input without RGB information. The proposed cascaded keypoint voting method, called RCVPose3D, is based upon a novel architectur
Externí odkaz:
http://arxiv.org/abs/2210.08123
Publikováno v:
In Neurocomputing 1 October 2024 600
Point cloud coding solutions have been recently standardized to address the needs of multiple application scenarios. The design and assessment of point cloud coding methods require reliable objective quality metrics to evaluate the level of degradati
Externí odkaz:
http://arxiv.org/abs/2108.02481
Point clouds (PCs) are a powerful 3D visual representation paradigm for many emerging application domains, especially virtual and augmented reality, and autonomous vehicles. However, the large amount of PC data required for highly immersive and reali
Externí odkaz:
http://arxiv.org/abs/2108.00054
An increased interest in immersive applications has drawn attention to emerging 3D imaging representation formats, notably light fields and point clouds (PCs). Nowadays, PCs are one of the most popular 3D media formats, due to recent developments in
Externí odkaz:
http://arxiv.org/abs/2006.03714
Reliable quality assessment of decoded point cloud geometry is essential to evaluate the compression performance of emerging point cloud coding solutions and guarantee some target quality of experience. This paper proposes a novel point cloud geometr
Externí odkaz:
http://arxiv.org/abs/2003.13669
Recently, point clouds have shown to be a promising way to represent 3D visual data for a wide range of immersive applications, from augmented reality to autonomous cars. Emerging imaging sensors have made easier to perform richer and denser point cl
Externí odkaz:
http://arxiv.org/abs/1912.09137
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