We are a computer graphics collective at UChicago led by Rana Hanocka, pursuing innovation at the intersection of 3D and Deep Learning.

People

News

2021-10-04

Kfir visits 3DL

2021-09-28

Will arrives!

starting 1st yr PhD student @ 3DL

2021-09-27

New topics course at Uchicago!

3D Geometry Processing & Computer Vision

2021-08-23

3DL website goes live.

Far from its final form...

2021-07-01

3DL is born!

But at the time it was the 3D Graphics Lab...

Research

Our research is focused on building artificial intelligence for 3D data, spanning the fields of computer graphics, machine learning, and computer vision. Deep Learning, the most popular form of artificial intelligence, has unlocked remarkable success on structured data (such as text, images, and video), and we are interested in harnessing the potential of these techniques to enable effective operation on unstructured 3D geometric data.

We have developed a convolutional neural network designed specifically for meshes, and also explored how to learn from the internal data within a single shape (for surface reconstruction, geometric texture synthesis, and point cloud consolidation) - and are interested in broader applications related to these areas. Additional research directions that we are aiming to explore include: intertwining human and machine-based creativity to advance our capabilities in 3D shape modeling and animation; learning with less supervision, for example to extract patterns and relationships from large shape collections; and making 3D neural networks more interpretable and explainable.

Orienting Point Clouds With Dipole Propagation
Gal Metzer, Rana Hanocka, Denis Zorin, Raja Giryes, Daniele Panozzo, and Daniel Cohen-Or
ACM Transactions on Graphics (Proc. SIGGRAPH) 2021
Learning Skeletal Articulations With Neural Blend Shapes
Peizhuo Li, Kfir Aberman, Rana Hanocka, Libin Liu, Olga Sorkine-Hornung, and Baoquan Chen
ACM Transactions on Graphics (Proc. SIGGRAPH) 2021
Self-Sampling for Neural Point Cloud Consolidation
Gal Metzer, Rana Hanocka, Raja Giryes, and Daniel Cohen-Or
Conditionally Accepted ACM Transactions on Graphics (TOG) 2021
Point2Mesh: A Self-Prior for Deformable Meshes
Gal Metzer, Rana Hanocka, Raja Giryes, and Daniel Cohen-Or
ACM Transactions on Graphics (Proc. SIGGRAPH) 2020
Deep Geometric Texture Synthesis
Amir Hertz*, Rana Hanocka*, Raja Giryes, and Daniel Cohen-Or (* joint first authors)
ACM Transactions on Graphics (Proc. SIGGRAPH) 2020
PointGMM: a Neural GMM Network for Point Clouds
Amir Hertz, Rana Hanocka, Raja Giryes, and Daniel Cohen-Or
CVPR 2020
MeshCNN: A Network with an Edge
Rana Hanocka, Amir Hertz, Noa Fish, Raja Giryes, Shachar Fleishman and Daniel Cohen-Or
ACM Transactions on Graphics (Proc. SIGGRAPH) 2019
Blind Visual Motif Removal from a Single Image
Amir Hertz, Sharon Fogel, Rana Hanocka, Raja Giryes, and Daniel Cohen-Or
CVPR 2019 (Oral)
ALIGNet: Partial-Shape Agnostic Alignment via Unsupervised Learning
Rana Hanocka, Noa Fish, Zhenhua Wang, Raja Giryes, Shachar Fleishman and Daniel Cohen-Or
ACM Transactions on Graphics (TOG) 2018
Fast and Easy Blind Deblurring Using an Inverse Filter and PROBE
Naftali Zon*, Rana Hanocka*, Nahum Kiryati (* joint first authors)
Proc. 17th International Conference on Computer Analysis of Images and Patterns (CAIP 2017)
Progressive Blind Deconvolution
Rana Hanocka, Nahum Kiryati
Proc. 16th International Conference on Computer Analysis of Images and Patterns (CAIP 2015)

Courses