Yuqun Wu
I am a first year PhD student in Computer Science department of University of Illinois at Urbana-Champaign, advised by Prof. Derek Hoiem. I also work closely with Prof. Shenlong Wang.
Prior to that, I also spent one year as a MS student and received my Bachelor of Science degree at UIUC, majoring in Computer Science & Statistics.
I did a summer intern at University of California San Diego in 2022, advised by Prof. Manmohan Chandraker
Email  / 
CV  / 
Linkedin
|
|
Research
I am interested in Computer Vision. My interest mainly lies in 3D geometry, including single-view depth completion, indoor 3D representation, and inverse rendering.
|
|
MonoPatchNeRF: Improving Neural Radiance Fields with Patch-based Monocular Guidance
Yuqun Wu*,
Jae Yong Lee*,
Chuhang Zou,
Shenlong Wang
, and Derek Hoiem
Project Page
, Preprint
Create 3D models that provide accurate geometry and view synthesis, partially closing the large geometric performance gap between NeRF and traditional MVS methods
|
|
Region-Based Representations Revisited
Michal Shlapentokh-Rothman*,
Ansel Blume*,
Yao Xiao,
Yuqun Wu,
Sethuraman T V,
Heyi Tao,
Jae Yong Lee,
Wilfredo Torres,
Yu-Xiong Wang,
and Derek Hoiem
CVPR 2024
Preprint
Investigate region based representation, combining class-agnostic segmentation from SAM and dense features from foundation models, for a wide variety of tasks, including semantic segmentation, object-based image retrieval, and multi-image analysis.
|
|
Sparse SPN: Depth Completion from Sparse Keypoints
Yuqun Wu*, Jae Yong Lee*, and Derek Hoiem
Preprint
Propose a novel method that outperforms existing depth completion pipelines given sparse keypoint depth, and reconstructs complete point clouds given SfM setups
|
|
QFF: Quantized Fourier Features for Neural Field Representations
Jae Yong Lee, Yuqun Wu, Chuhang Zou, Shenlong Wang, and Derek Hoiem
Preprint
Present Quantized Fourier Features (QFF), which encodes features in bins of Fourier features, and can result in smaller model size, faster training, and better quality outputs for various applications of neural representation
|
|