.png){kind=icon}
.png){kind=icon}
Return
A Flexible quantum point cloud representation supporting attribute filtering and downsampling
.png){kind=icon}
PRE
AI
DOI：10.1088/1402-4896/ae1db5.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In the field of quantum computer vision, significant achievements have been made in quantum image representation and processing. However, there are few methods for the quantum representation and processing of 3D models. In this paper, we propose a framework for preparing quantum 3D models, which makes the preparation process more comprehensive and overcomes the limitations of coordinate representation in existing quantum 3D models. Meanwhile, we propose a more flexible quantum point cloud representation (FQPC) that integrates this preparation framework with point cloud characteristics. FQPC enables the implementation of various classical point cloud operations in the quantum domain, including mirroring, color transformation, attribute filtering, and downsampling. In addition, FQPC uses a number of qubits determined by the axis-aligned bounding box of the point cloud to represent the coordinates, thereby reducing the use of qubits.
Journal
IF:
2.6
Papers:
4.3K
Citations:
2.5W