Ardyono Priyadi
Papers
1
Total Citations
2
H-Index
1
About
Ardyono Priyadi is a researcher whose work lies at the intersection of computer vision, deep learning, and 3D data processing. His key research areas include human body orientation classification, LiDAR-based point cloud analysis, and the development of efficient neural network architectures. Priyadi’s major contribution is the introduction of a modified CNN VoxNet model that employs depthwise separable convolution for voxel-driven body orientation classification. This innovation significantly reduces computational complexity while maintaining high accuracy in predicting human pose from 3D point cloud data, addressing a critical need in autonomous systems and human-robot interaction. His work demonstrates how LiDAR sensor technology can be leveraged to extract rich spatial information for real-world applications. With his most-cited paper garnering 2 citations since 2024, Priyadi is establishing a foundation for more efficient 3D recognition systems. His research holds particular promise for fields such as autonomous navigation, surveillance, and assistive robotics, where understanding human orientation is essential for safe and responsive system behavior.
Research Focus
Key Achievements
Top Papers
- 1