Yifan Wang
Yifan Wang
Home
News
Talks
Publications
CV
Light
Dark
Automatic
geometry processing
Geometric structures and why (and how) to use them in deep models?
In the last decades, the computer graphics community has invented many geometric structures to serve as useful representations to …
Jun 19, 2023 11:15 — 12:15
Vancouver
Toward editable implicit shapes
Implicit shape representation is an old concept in computer graphics mainly used in the surface reconstruction stage. Recently, it has …
Aug 7, 2022 16:30 — 17:30
Vancouver
“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
Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid Representations
Inter-dependent explicit representations for optimizing neural implicit surfaces
Wang Yifan
,
Shihao Wu
,
Cengiz Öztireli
,
Olga Sorkine-Hornung
PDF
Cite
Code
Video
Supplementary
IGL project page
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
×