Satoshi Komorita
Papers
1
Total Citations
6
H-Index
1
About
Satoshi Komorita is a leading researcher in 3D computer vision and autonomous perception, with a focus on efficient object detection from LiDAR point cloud data. His most influential work addresses a critical bottleneck in robotic systems: the high cost and labor intensity of manually labeling 3D point cloud datasets. Komorita pioneered a novel framework for 2D-to-3D label propagation, enabling the transfer of readily available image annotations to point cloud data. This approach significantly reduces the need for expensive manual 3D labeling, making it feasible to train robust object classifiers at scale. His 2018 paper on this method has garnered 6 citations, laying foundational groundwork for semi-supervised learning in 3D perception. Beyond this, Komorita’s contributions are vital for advancing autonomous driving, robotics, and augmented reality, where accurate 3D scene understanding is paramount. His work continues to influence researchers seeking to bridge the gap between 2D and 3D data domains, offering a practical pathway to more efficient and scalable object detection systems.
Research Focus
Key Achievements
Top Papers
- 12D to 3D Label Propagation For Object Detection In Point Cloud6 citations · 2018