Qingyong Hu is a DPhil candidate in Computer Science at the University of Oxford with eight years of experience focused on 3D computer vision and robotics, specializing in point cloud semantic and instance segmentation and local surface matching. He combines strong research supervision from leading academics with hands-on engineering, exemplified by his TensorFlow implementation and enhancements of the influential RandLA-Net model (CVPR 2020 / IEEE TPAMI 2021). His work spans developing core network architectures, training/evaluation pipelines, and practical applications for urban-scale 3D understanding, evidenced by participation in The Alan Turing Institute’s SenSat study. Based in Oxford, he brings both academic rigor and applied ML engineering to problems enabling safer autonomy and accurate digital twins.
8 years of coding experience
Dphil in Computer Science, Computer Science, Dphil in Computer Science, Computer Science at University of Oxford
🔥RandLA-Net in Tensorflow (CVPR 2020, Oral & IEEE TPAMI 2021)
Role in this project:
ML Engineer
Contributions:47 commits, 1 PR, 46 pushes in 1 year 7 months
Contributions summary:Qingyong appears to be focused on the implementation and modification of a RandLA-Net model for point cloud semantic segmentation. The commits reveal changes to the core network architecture, including encoder and decoder blocks, and loss calculations. The user is also involved in setting up the training and evaluation pipelines, with modifications to the main scripts and testing procedures.
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