Yifan Wang
Yifan Wang
Home
News
Talks
Publications
CV
Light
Dark
Automatic
point cloud
“Neuralize” geometry processing pipeline
Fueled by the proliferation of consumer-level 3D acquisition devices and the growing accessibility of shape modeling applications for …
Mar 9, 2022 16:30 — 17:30
online
Techniques for performing point-based inverse rendering
Cengiz Öztireli
,
Olga Sorkine-Hornung
,
Shihao Wu
,
Wang Yifan
Detail-Driven 3D Content Creation
A talk summarizing pretty much everything I’ve done in my IGL PhD.
Feb 3, 2021 15:00 — 16:00
online
Follow Toronto Geometry Colloquium
live recording
Pursuing high-resolution 3D Geometry with Deep Learning
Geometric details in 3D shapes is a defining factor in many industries such as AR/VR, VFX and design. However, creating high-fidelity …
Jun 11, 2020 21:00 — 21:30
online
Slides
Differentiable Surface Splatting for Point-based Geometry Processing
We propose a high-fidelity differentiable renderer for point clouds. We demonstrate how the proposed technique can be used to leverage contemporary deep neural networks to achieve state-of-the-art results in challenging geometry processing tasks.
Wang Yifan
, Felice Serena,
Shihao Wu
,
Cengiz Öztireli
,
Olga Sorkine-Hornung
PDF
Cite
Code
Project
Video
IGL Project Page
Patch-base progressive 3D Point Set Upsampling
We present a detail-driven deep neural network for point set upsampling. A high-resolution point set is essential for point-based …
Wang Yifan
,
Shihao Wu
,
Hui Huang
,
Daniel Cohen-Or
,
Olga Sorkine-Hornung
PDF
Cite
Code
Dataset
Supplemental
IGL project page
Point-based geometry processing
Making use of this extremely flexible yet unstructured form of shape represenation.
Cite
×