Long Hoang

About

Long Hoang is a researcher advancing the field of 3D computer vision and deep learning, with a primary focus on point cloud classification and 3D object recognition. His work addresses critical challenges in interpreting LiDAR data—a key technology for autonomous vehicles, robotics, and augmented reality. Hoang’s most influential contribution is **GSV-NET** (2022, 16 citations), a multi-modal deep learning network that significantly improves 3D point cloud classification accuracy by integrating complementary data representations. He also pioneered novel approaches combining geometric signatures with deep architectures, including the **Global Point Signature Plus with Deep Wide Residual Networks** (2021, 11 citations) and **Wave Kernel Signature with center point methods** (2019, 7 citations). These works demonstrate his ability to fuse classical 3D shape descriptors with modern neural networks, enabling more robust object classification and retrieval. Hoang’s research directly impacts practical applications in intelligent robotics, autonomous driving, and multimedia content processing. With a growing citation record and a clear trajectory toward solving fundamental 3D perception problems, he is establishing himself as a promising voice in the intersection of geometric deep learning and real-world sensing technologies.

Research Focus

Key Achievements

Top Papers

1. 1
   GSV-NET: A Multi-Modal Deep Learning Network for 3D Point Cloud Classification

   16 citations · 2022
2. 2
   A Deep Learning Method for 3D Object Classification and Retrieval Using the Global Point Signature Plus and Deep Wide Residual Network

   11 citations · 2021
3. 3
   A Deep Learning Method for 3D Object Classification Using the Wave Kernel Signature and A Center Point of the 3D-Triangle Mesh

   7 citations · 2019

Key Collaborators

Contact & Links