EE Seminar: Learning Signed Distance Representations for 3D Modeling
הרישום לסמינר יבוצע באמצעות סריקת הברקוד למודל (יש להיכנס לפני כן למודל, לא באמצעות האפליקציה)- הרישום מסתיים ב- 15:10
Registration to the seminar will be done by scanning the barcode for the Moodle (Please enter ahead to the Moodle, NOT by application)- Registration ends at 15:10
(The talk will be given in English)
Speaker: Dr. Lior Yariv
Faculty of Mathematics and Computer Science, Weizmann Institute
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011 hall, Electrical Engineering-Kitot Building |
Monday, October 27th, 2025
13:00 - 14:00
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Learning Signed Distance Representations for 3D Modeling
Abstract
A central question in learning 3D scenes is choosing an appropriate and efficient scene representation, one that faithfully represents the scene’s underlying geometry.
The goal of this talk is to present Signed Distance Functions (SDFs) as a core representation for 3D modeling. SDFs offer a flexible and expressive way to model a wide variety of closed surfaces with complex topology, while also allowing for straightforward surface extraction. Moreover, the geometric information encoded in SDFs facilitates accurate rendering and supports favorable reconstruction.
We present the potential of the SDF representation in addressing key problems in computer vision and computer graphics, focusing on two fundamental tasks: multi-view surface reconstruction and 3D generation. For multi-view surface reconstruction, we introduce novel methods for learning SDF-based representations directly from 2D images, by integrating SDFs into differentiable volume rendering pipelines. Towards 3D generation, we present a scalable and expressive surface representation tailored for training flow-based generative models on large-scale 3D shape datasets. Together, these contributions demonstrate the versatility and effectiveness of SDFs in addressing key challenges in learning meaningful 3D representations under different supervision settings.
Short Bio
Lior Yariv is starting a Postdoctoral Researcher position at Stanford University, where she will join Prof. Gordon Wetzstein's group.
She recently completed her PhD in Computer Science and Applied Mathematics at the Weizmann Institute of Science, supervised by Prof. Yaron Lipman.
Her main research interest is in devising deep learning methods for various 3D shape analysis and synthesis tasks.
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