Hengyi Wang  |  ηŽ‹ζ’δΈ€

I am a PhD student (Jan. 2023 - 2027), working with Prof. Lourdes Agapito at CDT in Foundational AI at University College London. My research topic is Neural Surface Reconstruction from Video.

I obtained my Master degree with thesis on "Neural Representations for 3D Reconstruction" at UCL, suprvised by Prof. Lourdes Agapito. Prior to UCL, I obtained a bachelor degree at joint degree programme between QMUL and BUPT, worked with Prof. Changejae Oh.

Feel free to drop me a message if you would like to know more about my research or just up for a casual chat :)

Email  /  CV  /  Google Scholar  /  Linkedin  /  Github  /  Notebooks

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Research

I'm interested in computer vision, deep learning, and generative models. My current research focuses on 3D perception and understanding. I'm especially passionate about the challenge of perceiving and simultaneously 'dreaming up' the physical world from images.

MorpheuS: Neural Dynamic 360Β° Surface Reconstruction from Monocular RGB-D Video
Hengyi Wang, Jingwen Wang, Lourdes Agapito
CVPR, 2024
project page / arXiv / code / video

We present MorpheuS, a dynamic scene reconstruction method that leverages neural implicit representations and diffusion priors for achieving 360Β° reconstruction of a moving object from a monocular RGB-D video.

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 present a framework to estimate phyiscal properties of the house-hold containers and its fillings from RGB-D and audio inputs. Rank 1st in the CORSMAL Challenge 2022.

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
  • Conference Reviewer: IROS
  • Journal Reviewer: RA-L
  • Teaching assistant: Machine Vision, Image Processing


Thanks Jon Barron for the website.