MBZUAI ·
A researcher at the University of Oxford presented new findings on 3D neural reconstruction. The talk introduced a dataset comprising real-world video captures with perfect 3D models. A novel joint optimization method refines camera poses during the reconstruction process. Why it matters: High-quality 3D reconstruction has broad applicability to robotics and computer vision applications in the region.
MBZUAI ·
Egor Zakharov from ETH Zurich AIT lab will present research on creating controllable and detailed 3D head avatars using data from consumer-grade devices. The presentation will cover high-fidelity image-based facial reconstruction/animation and video-based reconstruction of detailed structures like hairstyles. He will showcase integrating human-centric assets into virtual environments for real-time telepresence and entertainment. Why it matters: This research contributes to advancements in digital human modeling and telepresence, with applications in communication and gaming within the region.
MBZUAI ·
Dr. Xiaoming Liu from Michigan State University discussed computer vision techniques for 3D world understanding at a talk hosted by MBZUAI. The talk covered 3D reconstruction, detection, depth estimation, and velocity estimation, with applications in biometrics and autonomous driving. Dr. Liu also touched on anti-spoofing and fair face recognition research at MSU's Computer Vision Lab. Why it matters: Showcasing international experts and research directions helps to catalyze computer vision and 3D understanding research efforts within the UAE's AI ecosystem.
KAUST ·
KAUST's Peter Wonka discusses the challenges and advancements in creating data-rich, three-dimensional maps for various applications. His team is working with Boeing on 3D modeling tools for aerospace design. KAUST-funded FalconViz uses UAV drones to create 3D maps of disaster areas for first responders. Why it matters: This highlights KAUST's contribution to cutting-edge 3D modeling and its practical applications in industries like aerospace and disaster response in the region.
MBZUAI ·
Pascal Fua from EPFL presented an approach to implementing convolutional neural nets that output complex 3D surface meshes. The method overcomes limitations in converting implicit representations to explicit surface representations. Applications include single view reconstruction, physically-driven shape optimization, and bio-medical image segmentation. Why it matters: This research advances geometric deep learning by enabling end-to-end trainable models for 3D surface mesh generation, with potential impact on various applications in computer vision and biomedical imaging in the region.
KAUST ·
The KAUST Visual Computing (KAUST RC-VC) – Modeling and Reconstruction conference featured speakers from Simon Fraser University, Caltech, Cornell University, and Autodesk. Presentations covered topics like networking topology, shape matching and modeling, data-driven interpolation of optical properties, and computer graphics. Why it matters: The conference highlights KAUST's role in fostering international collaboration and advancing research in visual computing and related fields within Saudi Arabia.
KAUST ·
KAUST researchers used 3D mapping technology via remote control helicopter to survey and create detailed renderings of Jeddah's Al Balad, a UNESCO World Heritage Site. The team, from KAUST's Visual Computer Center and FalconViz, captured high-definition images from about 50 meters above street level. This enabled the creation of accurate 3D models, showing building shifts and potential problems for urban planners. Why it matters: This method provides a rapid and accurate way to document and preserve historical landmarks, especially in areas where traditional surveying is difficult or infeasible, aiding in cultural heritage preservation efforts.
MBZUAI ·
Marc Pollefeys from ETH Zurich and Microsoft Spatial AI Lab will discuss building 3D environment representations for assisting humans and robots. The talk covers visual 3D mapping, localization, spatial data access, and navigation using geometry and learning-based methods. It also explores building rich 3D semantic representations for scene interaction via open vocabulary queries leveraging foundation models. Why it matters: Advancements in spatial AI and 3D scene understanding are critical for enabling more capable robots and AI assistants in various applications within the region.