Kanokphan Lertniphonphan
Papers
1
Total Citations
6
H-Index
1
About
Kanokphan Lertniphonphan is a researcher specializing in computer vision and 3D perception, with a particular focus on object detection from LiDAR point cloud data—a critical area for autonomous systems and robotics. Her most cited work introduces a novel framework for 2D-to-3D label propagation, addressing the significant challenge of costly and time-consuming manual annotation of point cloud datasets. By leveraging existing 2D image labels to automatically generate 3D labels, her approach enables more efficient training of object detection classifiers for robotic systems, reducing the barrier to creating large-scale annotated datasets. This contribution has garnered attention in the field, with her seminal 2018 paper accumulating 6 citations and establishing a foundation for semi-automated annotation pipelines. Lertniphonphan's work sits at the intersection of practical machine learning deployment and data efficiency, offering solutions that accelerate the development of perception systems in autonomous driving and robotics. Her research continues to influence methods for bridging the gap between 2D and 3D vision tasks, making her a notable voice in the advancement of scalable object detection technologies.
Research Focus
Key Achievements
Top Papers
- 12D to 3D Label Propagation For Object Detection In Point Cloud6 citations · 2018