Wang Chia-Hung
Moonshot Vision

Looking for friends

who also want to build autonomous factories on the Moon and master space energy systems so silicon-based lifeforms can independently explore the universe and decode unknown laws of nature.

Biography

Wang Chia-Hung earned a B.S. in Electrical Engineering and a B.Fin. from NTUST in 2018, then completed an M.S. in CSIE at NTU in 2021. His work centers on AI perception, deep learning, and computer vision.

Current Focus Autonomous driving
Wang Chia-Hung portrait

Education

National Taiwan University triangle banner National Taiwan University logo National Taiwan University of Science and Technology logo
National Taiwan University (NTU) 2019-09 to 2021-08
M.S. in CSIE Intelligent Robot Lab EECS
National Taiwan University of Science and Technology (NTUST) 2015-09 to 2018-08
B.S. in EE B.Fin. Minor in BA

Work Experience

Founding Team Member, Technical Expert
G.O.R.T. (Green Origin Ready Technologies)
2025-02 to Present
Multi-modal Perception Algorithm Engineer
Haomo
2023-12 to 2025-02
Deep Learning Algorithm Engineer
AutoX
2021-10 to 2023-12
Assistant
Taichung Pei Hsin Maker Education and Technology Center
2018-09 to 2019-07
Intern
AAEON
2018-07 to 2018-08
Intern
HIWIN
2015-03 to 2015-07

Publications

VoPiFNet: Voxel-Pixel Fusion Network for Multi-Class 3D Object Detection SCI Q1
Wang, Chia-Hung; Chen, Hsueh-Wei; Chen, Yi; Hsiao, Pei-Yung; Fu, Li-Chen
IEEE Transactions on Intelligent Transportation Systems (T-ITS), 2024.
A Novel Regional Fusion Network for 3D Object Detection based on RGB Images and Point Clouds
Chen, Hung-Hao; Wang, Chia-Hung; Chen, Hsueh-Wei; Hsiao, Pei-Yung; Fu, Li-Chen; Su, Yi-Feng
CS & IT Conference Proceedings, 2021.
電腦硬體裝修乙級術科第二版 [Computer Maintenance, 2/e]
劉政鑫, 王嘉鴻
碁峰資訊, 2016.
電腦硬體裝修乙級術科 [Computer Maintenance]
劉政鑫, 王嘉鴻
碁峰資訊, 2015.

Research

Focus

  • • 3D Object Detection for Autonomous Driving
  • • Multi-modal Sensor Fusion: LiDAR, RGB Camera, Radar
  • • Tools: PyTorch, Python

Links

KITTI 3D Object Detection Evaluation

  • • Our VPFNet underwent evaluation on 2021-06-20 using the official testing set.
  • • Achieved 65.99% mAP for the multi-class 3D object detection task across various difficulty levels, surpassing the state-of-the-art performance of all previously published methods.
  • • Ranks 1st on the leaderboard for pedestrian class detection (through 2022-03-15) (9 months).
KITTI rank result3D detection visualization

Credentials

Certificates

  • CompTIA Strata IT Fundamentals, 2015-05-17
  • Certificate of Automation Engineer, TAIROA, 2015-01-10

Awards

  • Certificate of Achievement, National Taiwan University of Science and Technology, 2018-10-05
  • Honorable Mention, National Electronic Design Creative Competition, 2018-04-14
  • Certificate of Achievement, National Taiwan University of Science and Technology, 2017-03-17
  • Certificate of Achievement, National Taiwan University of Science and Technology, 2016-10-13
  • Merit award, General Examination of Introduction to Computer, 2016-06-03
  • Certificate of Achievement, National Taiwan University of Science and Technology, 2016-03-21
  • First place, Computer Maintainance, National High School Skills Competition, Ministry of Education, 2014-11-28
  • First place, Network Diagnosis Skills Competition, Cisco Networking Academy, 2014-08-22
  • Third place, IT Network Systems Administration, Ministry of Labor, 2014-04-27