38 citations · 60 across the 5 of their papers we have counts for
6 papers
Enhancing Performance of Point Cloud Completion Networks with Consistency Loss
Kevin Tirta Wijaya, Christofel Rio Goenawan, Seung-Hyun Kong
Point cloud completion networks are conventionally trained to minimize the disparities between the completed point cloud and the ground-truth counterpart. However, an incomplete ob…
Enhanced K-Radar: Optimal Density Reduction to Improve Detection Performance and Accessibility of 4D Radar Tensor-based Object Detection
Dong-Hee Paek, Seung-Hyun Kong, Kevin Tirta Wijaya
Recent works have shown the superior robustness of four-dimensional (4D) Radar-based three-dimensional (3D) object detection in adverse weather conditions. However, processing 4D R…
Row-wise LiDAR Lane Detection Network with Lane Correlation Refinement
Dong-Hee Paek, Kevin Tirta Wijaya, Seung-Hyun Kong
Lane detection is one of the most important functions for autonomous driving. In recent years, deep learning-based lane detection networks with RGB camera images have shown promisi…
K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions
Dong-Hee Paek, Seung-Hyun Kong, Kevin Tirta Wijaya
Unlike RGB cameras that use visible light bands (384769 THz) and Lidars that use infrared bands (361331 THz), Radars use relatively longer wavelength radio bands (77$\s…
Advanced Feature Learning on Point Clouds using Multi-resolution Features and Learnable Pooling
Kevin Tirta Wijaya, Dong-Hee Paek, Seung-Hyun Kong
Existing point cloud feature learning networks often incorporate sequences of sampling, neighborhood grouping, neighborhood-wise feature learning, and feature aggregation to learn…
K-Lane: Lidar Lane Dataset and Benchmark for Urban Roads and Highways
Donghee Paek, Seung-Hyun Kong, Kevin Tirta Wijaya
Lane detection is a critical function for autonomous driving. With the recent development of deep learning and the publication of camera lane datasets and benchmarks, camera lane d…