Papers
1
Total Citations
11
H-Index
1
About
Lee Jun Min is an emerging leader in autonomous driving perception, with a focused expertise in multi-sensor fusion for 3D object detection. His most-cited work, "BAFusion: Bidirectional Attention Fusion for 3D Object Detection Based on LiDAR and Camera" (2024), tackles a critical bottleneck in the field: the rigid, projection-matrix-based alignment of LiDAR point clouds and camera images. By introducing a bidirectional attention mechanism, Lee’s approach enables dynamic, learnable feature interaction between the two modalities, overcoming the limitations of static geometric alignment. This innovation has already garnered 11 citations in its first year, signaling strong early impact. Lee’s research is particularly notable for its practical relevance to autonomous driving and robotics, where robust sensor fusion is essential for safety and reliability. His work stands out for moving beyond conventional fusion pipelines, offering a more flexible and context-aware framework that adapts to complex real-world scenes. As a researcher, Lee Jun Min is helping to define the next generation of perception systems, where deep learning and attention mechanisms converge to create safer, more intelligent autonomous agents.
Research Focus
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Top Papers
- 1