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

Electrical Engineering Systems Seminar

Speaker: Tamir Cohen

M.Sc. student under the supervision of Dr. Hadar Averbuch-Elor and Prof. Shai Avidan

Sunday, 25th May 2025, at 15:00

Room 011, Kitot Building, Faculty of Engineering

Scene Grounding In the Wild

Abstract

Structure from motion techniques typically produce multiple partial 3D reconstructions when applied over Internet photo collections that capture the world's landmarks. In this work, we present a technique that grounds each partial reconstruction to a complete 3D reference model of the scene, which can be easily obtained from dense imagery acquired from Google Earth Studio. We represent the reference model using 3D Gaussian Splatting, and propose a robust optimization-based framework that predicts the 6DoF+scale alignment between the reconstructed real-world images and the reference model. Our approach is built upon the premise that Internet images and views rendered from low-quality reference models capture the same underlying semantics of the scene, even though their visual appearance highly varies. We therefore augment the 3D Gaussians with semantic features, which are used to guide our optimization procedure. To evaluate our method, we contribute the WikiEarth dataset, that registers over thirty existing partial 3D reconstructions with reference models. Our evaluation shows that our approach significantly outperforms alternative baselines, allowing for grounding partial 3D reconstructions into a global unified model. We will release our code, data and trained models.

ORACLE-Grasp: Zero-Shot Task-Oriented Robotic Grasping using Large Multimodal Models

Monday May 26th 2025 at 15:00 

Wolfson Building of Mechanical Engineering, Room 206 

Abstract:

Grasping unknown objects in unstructured envi- ronments remains a fundamental challenge in robotics,

requir- ing both semantic understanding and spatial reasoning. Existing methods often rely on dense training datasets or explicit geo- metric modeling, limiting their scalability to real-world tasks. Recent advances in Large Multimodal Models (LMMs) offer new possibilities for integrating vision and language under- standing, but their application to autonomous robotic grasping remains largely unexplored. We present ORACLE-Grasp, a zero-shot framework that leverages LMMs as semantic oracles to guide grasp selection without requiring additional training or human input. The system formulates grasp prediction as a structured, iterative decision process, using dual-prompt tool calling to first extract high-level object context and then select task-relevant grasp regions. By discretizing the image space and reasoning over candidate areas, ORACLE-Grasp mitigates the spatial imprecision common in LMMs and produces human- like, task-driven grasp suggestions. Early stopping and depth- based refinement steps further enhance efficiency and physical grasp reliability. Experiments demonstrate that the predicted grasps achieve low positional and orientation errors relative to human-annotated ground truth and lead to high success rates in real-world pick-up tasks. These results highlight the potential of combining language-driven reasoning with lightweight vision techniques to enable robust, autonomous grasping without task- specific datasets or retraining.

Bio:

Avi holds a B.Sc. in Mechanical Engineering and is currently completing his M.Sc. in Mechanical Engineering at Tel Aviv University, under the supervision of Prof. Avishai Sintov. His academic work focuses on the intersection of robotics, artificial intelligence, computer vision and genAI. Alongside his studies, Avi works as a Computer Vision Engineer at Qapture-Zimark, where he applies advanced visual perception techniques to real-world industrial challenges. He is passionate about developing intelligent systems that bridge the gap between visual understanding and physical interaction in robotic platforms.

?Did Informal Contact Tracing Make COVID-19 Quarantines Better
Rottem Botton, M.Sc. student at School of Industrial & Intelligent Systems Engineering
Advisor: Prof. Eran Toch

Join us with Zoom

Abstract:
In the wake of the COVID-19 pandemic, many countries swiftly implemented contact tracing and quarantine measures as primary interventions to curb the spread of the virus. In Israel, official guidelines required self-isolation for anyone testing positive for COVID-19, returning from abroad, or coming into contact with a confirmed COVID-19 case. While formal contact tracing by health authorities, using mobile technology or manual methods, is widely explored, little is known about the extent and impact of informal contact tracing, which relies on direct interaction with an individual’s social networks and personal responsibility for voluntary quarantining. Based on a representative sample of Israeli adults (n = 506), our study examines the extent of informal contact tracing and voluntary quarantines, as well as their impact on people’s perceptions. Our findings show that informal contact tracing accounts for approximately one-third of the reported first quarantines among our participants. It is viewed as significantly more justified and slightly less distressful than those initiated due to formal contact tracing, even when controlling for factors such as trust in health authorities, stress, and demographics. These findings suggest that promoting positive freedom and social norms can have beneficial effects, many of which remain underexplored.

Bio:
Rottem Botton is an M.Sc. student at the School of Industrial Engineering & Intelligent Systems Engineering, where she researches impromptu contact tracing at the IWIT Lab under the supervision of Prof. Eran Toch. She holds a B.Sc. degree in Industrial Engineering and Management from Tel Aviv University.