Spatial AI Lab – Zurich: Advancing Computer Vision and Robotics Research

Spatial AI Lab – Zurich: Advancing the Frontiers of Artificial Intelligence

This document outlines the research conducted at the Spatial AI Lab in Zurich, a key component of Microsoft Research. The lab focuses on cutting-edge advancements in artificial intelligence, particularly in areas related to spatial understanding, computer vision, and robotics. The content details various research areas, publications, people involved, and contact information, providing a comprehensive overview of the lab's contributions to the field.

Research Areas:

The Spatial AI Lab's research spans several critical domains within AI and computer vision:

• Intelligence:

  • Artificial Intelligence
  • Audio & Acoustics
  • Computer Vision
  • Graphics & Multimedia
  • Human-Computer Interaction
  • Human Language Technologies
  • Search & Information Retrieval

• Systems:

  • Data Platforms and Analytics
  • Hardware & Devices
  • Programming Languages & Software Engineering
  • Quantum Computing
  • Security, Privacy & Cryptography
  • Systems & Networking

• Theory:

  • Algorithms
  • Mathematics

• Other Sciences:

  • Ecology & Environment
  • Economics
  • Medical, Health & Genomics
  • Social Sciences
  • Technology for Emerging Markets

Key Publications and Research Highlights:

The lab has produced a significant body of work, with a strong emphasis on computer vision and its applications. Notable publications include:

• LightGlue: Local Feature Matching at Light Speed: This work focuses on efficient and accurate local feature matching, a fundamental problem in computer vision, particularly relevant for tasks like image stitching, 3D reconstruction, and augmented reality.
• Tracking by 3D Model Estimation of Unknown Objects in Videos: This research addresses the challenge of tracking objects in videos by estimating their 3D models, even when the objects are not pre-defined.
• Vanishing Point Estimation in Uncalibrated Images with Prior Gravity Direction: This paper presents a method for estimating vanishing points in images, crucial for understanding scene geometry and perspective, by leveraging prior knowledge of gravity direction.
• Human from Blur: Human Pose Tracking from Blurry Images: This research tackles the difficult problem of estimating human pose from blurry images, which is common in real-world scenarios.
• SGAligner: 3D Scene Alignment with Scene Graphs: This work focuses on aligning 3D scenes using scene graphs, enabling better understanding and manipulation of complex environments.
• RLSAC: Reinforcement Learning enhanced Sample Consensus for End-to-End Robust Estimation: This paper introduces a novel approach that combines reinforcement learning with RANSAC for robust estimation tasks in computer vision.
• Learning to Simulate Realistic LiDARs: This research focuses on generating realistic LiDAR data through simulation, which is vital for training autonomous driving systems and other robotics applications.
• 3D Face Reconstruction with Dense Landmarks: This work presents methods for reconstructing 3D faces with high accuracy, including dense landmark information, important for applications in facial recognition and animation.
• LaMAR: Benchmarking Localization and Mapping for Augmented Reality: This paper introduces a benchmark for evaluating localization and mapping algorithms in augmented reality, a critical step for developing robust AR systems.
• Panoptic Multi-TSDFs: a Flexible Representation for Online Multi-resolution Volumetric Mapping and Long-term Dynamic Scene Consistency: This research proposes a new representation for volumetric mapping that allows for online, multi-resolution updates and maintains consistency in dynamic scenes.

People and Leadership:

The Spatial AI Lab is led by Marc Pollefeys, Partner Director of Science. The lab comprises a team of researchers and engineers dedicated to pushing the boundaries of AI. The publications list includes contributions from numerous researchers, such as Pablo Speciale, Ondrej Miksik, Mihai Dusmanu, Remi Pautrat, Silvano Galliani, Christoph Vogel, Rui Wang, Jeffrey Delmerico, Vibhav Vineet, Ashley Feniello, Dan Bohus, Haiyan Zhang, Matthew Johnson, Neel Joshi, Shuo Chen, Sean Andrist, Lukas Gruber, Ishani Chakraborty, Tadas Baltrusaitis, Charlie Hewitt, and Mahdi Rad.

Location and Contact:

The Spatial AI Lab is located at:

Seestrasse 356, Zurich 8038 Switzerland

Contact information and social media links are provided for engagement and further information.

Social Media and Engagement:

The lab actively engages with the research community through various platforms:

• Social Media: Facebook, X (formerly Twitter), LinkedIn, YouTube, Instagram.
• Community Engagement: Microsoft Research blog, podcasts, forums, and newsletters.
• Sharing: Options to share content on X, Facebook, LinkedIn, and Reddit.

Navigation and Resources:

The page provides extensive navigation options, including:

• Lab Information: Overview, People, Publications, News & Features, Collaborations, Events, Videos, Projects.
• Microsoft Research Navigation: Links to various research areas, programs, events, and about sections.
• Microsoft Ecosystem: Links to Microsoft products, services, and industry solutions.
• Search Functionality: A search bar to find specific content within Microsoft Research.

Image and Visuals:

The page features an image with the dimensions 6573x1432, likely a background or illustrative element. The content also includes links to profile pictures of researchers, such as Marc Pollefeys.

Overall Focus:

The Spatial AI Lab in Zurich is a hub for advanced research in computer vision, spatial computing, and AI, contributing significantly to fields like robotics, augmented reality, and 3D understanding through its innovative publications and dedicated team.