LLaVA-3D revolutionises 3D vision by extending 2D large multimodal models with spatial awareness, enabling 3.5x faster training and state-of-the-art 3D understanding. —  @meisshaily #ArtificialIntelligence# #AI# #TechNews# #TechNology# #VisionModels# #Tech#

Ask a 3D vision-language model what's near the table and in front of the curtain, and it might guess "sewing machine." The right answer is a tray rack. CVP (UC San Diego + Lambda, WACV 2026) fixes this with a target-affinity token for task-relevant objects and an allocentric grid for global context. Against Video-3D-LLM: • SQA3D EM: 58.6 → 62.3 • Scan2Cap CIDEr: 83.8 → 90.5 • Better on all 5 benchmarks tested Full results across ScanQA, SQA3D, ScanRefer, Multi3DRefer, and Scan2Cap, plus how the central/peripheral split works:

Unlocking the secret to faster, smarter 3D #vision# is no longer a distant dream. If you’ve ever wondered how to enhance 3D understanding without the complexity of traditional pipelines, LLaVA-3D offers a breakthrough.  — @meisshaily #ArtificialIntelligence# #AI# #TechNology# #Tech# #TechNews#

A full MIT course on visual autonomous navigation. If you work on robotics, drones, or self-driving systems, this one is worth bookmarking‼️ MIT’s Visual Navigation for Autonomous Vehicles course covers the full perception-to-control stack, not just isolated algorithms. What it focuses on: • 2D and 3D vision for navigation • Visual and visual-inertial odometry for state estimation • Place recognition and SLAM for localization and mapping • Trajectory optimization for motion planning • Learning-based perception in geometric settings All material is available publicly, including slides and notes. 📍 If you know other solid resources on vision-based autonomy, feel free to share them. —- Weekly robotics and AI insights. Subscribe free: