Itai Lang

I am a Post Doctoral Researcher at the University of Chicago, working with Assistant Professor Rana Hanocka. I did my PhD in Electrical Engineering at Tel Aviv University with the guidance of Professor Shai Avidan. During my PhD, I interned at Google Research, where I worked with Michael Rubinstein.

I am a passionate researcher who seeks creative solutions to new problems and innovative applications. My research is focused on developing artificial intelligence for geometry processing, spanning the fields of computer vision, computer graphics, and machine learning.

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SAGA: Spectral Geometric Adversarial Attack on 3D Meshes
Tomer Stolik*, Itai Lang*, Shai Avidan
(*Equal contribution)
ICCV, 2023
project page / arXiv / code

We apply a low frequency perturbation in the spectral shape domain to alter the reconstruction by a victim mesh autoencoder to a desired output geometry.

SCOOP: Self-Supervised Correspondence and Optimization-Based Scene Flow
Itai Lang, Dror Aiger, Forrester Cole, Shai Avidan, Michael Rubinstein
CVPR, 2023
project page / arXiv / video / code

We use pure correspondence learning and direct refinement optimization to predict a highly accurate scene flow while using a fraction of the training data.

3D Highlighter: Localizing Regions on 3D Shapes via Text Descriptions
Dale Decatur, Itai Lang, Rana Hanocka
CVPR, 2023 (Highlight Presentation)
project page / arXiv / video / code

We synthesize colors over a 3D shape and use CLIP supervision to localize semantic regions using open vocabulary text prompts.

DPC: Unsupervised Deep Point Correspondence via Cross and Self Construction
Itai Lang*, Dvir Ginzburg*, Shai Avidan, Dan Raviv
(*Equal contribution)
3DV, 2021
arXiv / video / code

We use latent similarity and the point coordinates themselves to construct one point cloud by the other and achive an accurate dense matching with a small training data amount and without any correspondence supervision.

Geometric Adversarial Attacks and Defenses on 3D Point Clouds
Itai Lang, Uriel Kotlicki, Shai Avidan
3DV, 2021
arXiv / video / code

We perturb an input point cloud to attack an autoencoder model and change the reconstructed geometry to a different selected target shape.

SampleNet: Differentiable Point Cloud Sampling
Itai Lang, Asaf Manor, Shai Avidan
CVPR, 2020 (Oral Presentation)
arXiv / video / code

We propose a differentiable relaxation to the sampling operation that enables learning a task-oriented sampling model in an end-to-end manner.

Learning to Sample
Oren Dovrat*, Itai Lang*, Shai Avidan
(*Equal contribution)
CVPR, 2019
arXiv / code

We propose a data-driven sampling approach for 3D point clouds that selects the most suitable subset of points for a downstream task.

Reviewer for CVPR 2020, SIGGRAPH 2022, CVPR 2023 (Outstanding Reviewer), SIGGRAPH Asia 2023.

Reviewer for TPAMI, TVCG, TCSVT, CAG.

This webpage was adapted from Jon Barron's webpage. We thank Jon Barron for sharing his source code.