Hengyi Wang (ηŽ‹ζ’δΈ€)

I am a PhD student at CDT in Foundational AI at University College London, supervised by Prof. Lourdes Agapito. My research topic is Neural Surface Reconstruction from Video. Prior to start my PhD, I obtain an MSc degree at UCL and a bachelor degree at joint degree programme between QMUL and BUPT, worked with Prof. Changejae Oh.

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Research

I'm interested in computer vision, image processing, and machine learning related areas.

Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM
Hengyi Wang, Jingwen Wang, Lourdes Agapito
CVPR, 2023
project page / arXiv / code / video

We present a neural RGB-D SLAM system, Co-SLAM, which performs robust camera tracking and high-fidelity surface reconstruction in real time. This paper extends the work presented in my MSc thesis.

Boosting Video Object Segmentation Based on Scale Inconsistency
Hengyi Wang, Changjae Oh
ICME, 2022
project page / arXiv / code / video

We present a refinement framework to boost the performance of pre-trained semi-supervised video object segmentation (VOS) models based on scale inconsistency. Our work consists of a multi-scale attention module with the corresponding self-supervised online adaptation strategy.

clean-usnob Improving Generalization of Deep Networks for Estimating Physical Properties of Containers and Fillings
Hengyi Wang*, Chaoran Zhu*, Ziyin Ma, Changjae Oh
ICASSP Grand Challenge: Rank 1st, 2022
challenge page / arXiv / code

We use a MobileNet as a backbone model to estimate the phyiscal physical properties of house-hold containers and their fillings. A data augmentation strategy with consistency measurement is proposed to improve the generalization ability of our model.

Thesis
Neural Representations for 3D Reconstruction
Master thesis at UCL, 2022
paper / code

Built a neural RGB-D surface reconstruction system based on joint coordinate and sparse parametric encoding.

Notes
clean-usnob Reading: CS236 Deep Generative Models
Stefano Ermon, Yang Song,
notes

Academic Services
  • Journal Reviewer: RA-L
  • Teaching assistant: Machine Vision, Image Processing


Thanks Jon Barron for the website.