5 papers
A Comprehensive Survey on Deep Learning-Based LiDAR Super-Resolution for Autonomous Driving
June Moh Goo, Zichao Zeng, Jan Boehm
LiDAR sensors are often considered essential for autonomous driving, but high-resolution sensors remain expensive while affordable low-resolution sensors produce sparse point cloud…
Real-Time LiDAR Super-Resolution via Frequency-Aware Multi-Scale Fusion
June Moh Goo, Zichao Zeng, Jan Boehm
LiDAR super-resolution addresses the challenge of achieving high-quality 3D perception from cost-effective, low-resolution sensors. While recent transformer-based approaches like T…
Exploring Single Domain Generalization of LiDAR-based Semantic Segmentation under Imperfect Labels
Weitong Kong, Zichao Zeng, Di Wen +5
Accurate perception is critical for vehicle safety, with LiDAR as a key enabler in autonomous driving. To ensure robust performance across environments, sensor types, and weather c…
Checkerboard Target Measurement in Unordered Point Clouds with Coloured ICP
June Moh Goo, Jialun Li, Darmawan Wicaksono +1
In this work, we investigate the problem of measuring a the centre checkerboard target in an 3D point cloud. This is an important problem which has applications in registration, lo…
Hybrid-Segmentor: A Hybrid Approach to Automated Fine-Grained Crack Segmentation in Civil Infrastructure
June Moh Goo, Xenios Milidonis, Alessandro Artusi +2
Detecting and segmenting cracks in infrastructure, such as roads and buildings, is crucial for safety and cost-effective maintenance. In spite of the potential of deep learning, th…