Jinman Kim
Papers
1
Total Citations
76
H-Index
1
About
Jinman Kim is a leading researcher in 3D computer vision and deep learning, with a focus on advancing object recognition through innovative neural network architectures. His most cited work, "SparseVoxNet: 3-D Object Recognition With Sparsely Aggregation of 3-D Dense Blocks" (2022, 76 citations), introduces a novel convolutional neural network approach that efficiently processes 3D voxel data by sparsely aggregating dense blocks. This breakthrough significantly improves the accuracy and computational efficiency of 3D object recognition, enabling practical applications in robotics, augmented reality, and autonomous systems. Kim’s contributions address critical challenges in analyzing complex 3D models from multi-view images and depth data, making his methods highly influential in both academic research and industry. His work has garnered substantial attention, with citations reflecting its impact on advancing 3D vision technologies. By bridging the gap between theoretical deep learning and real-world 3D perception tasks, Kim continues to shape the future of intelligent systems that interact with and understand three-dimensional environments.
Research Focus
Key Achievements
Top Papers
- 1