no code implementations • 27 Jul 2021 • Sungmin Woo, Dogyoon Lee, Sangwon Hwang, Woojin Kim, Sangyoun Lee
In this paper, we present Multidimensional Kernel Convolution (MKConv), a novel convolution operator that learns to transform the point feature representation from a vector to a multidimensional matrix.
Ranked #13 on 3D Part Segmentation on ShapeNet-Part
1 code implementation • 14 Feb 2021 • Minhyeok Lee, Junhyeop Lee, Dogyoon Lee, Woojin Kim, Sangwon Hwang, Sangyoun Lee
Modern deep learning methods achieve high performance in lane detection, but it is still difficult to accurately detect lanes in challenging situations such as congested roads and extreme lighting conditions.
Ranked #42 on Lane Detection on CULane
no code implementations • 27 May 2020 • Sungmin Woo, Sangwon Hwang, Woojin Kim, Junhyeop Lee, Dogyoon Lee, Sangyoun Lee
Recently, researchers have been leveraging LiDAR point cloud for higher accuracy in 3D vehicle detection.
no code implementations • 22 Nov 2018 • Chaoyun Zhang, Rui Li, Woojin Kim, Daesub Yoon, Paul Patras
Experiments conducted with a dataset that we collect in a mock-up car environment demonstrate that the proposed InterCNN with MobileNet convolutional blocks can classify 9 different behaviors with 73. 97% accuracy, and 5 'aggregated' behaviors with 81. 66% accuracy.