Page 9: Research news on Computational 3D vision

Computational 3D vision concerns algorithms and sensor systems that infer three-dimensional structure, motion, and semantics from visual and related signals. Methods span monocular and multi-view 3D reconstruction, depth estimation, inverse rendering, and 4D scene capture, often integrating LiDAR, radar, infrared, and event or neuromorphic sensors. Deep learning architectures and data-driven simulation play central roles in segmentation, pose estimation, anomaly detection, and novel view synthesis, enabling robust perception, mapping, and editing of complex environments for robotics, autonomous systems, and immersive displays.

Automotive

With some help from AI, your next move can be predicted

AI might know where you're going before you do. Researchers at Northeastern University used large language models, the kind of advanced artificial intelligence normally designed to process and generate language, to predict ...

Computer Sciences

Creating realistic 3D scenes from everyday online photos

A new approach is making it easier to visualize lifelike 3D environments from everyday photos already shared online, opening new possibilities in industries such as gaming, virtual tourism and cultural preservation.

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