Page 2: 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.

Machine learning & AI

Researchers develop intelligent 3D twins of crime scenes

A crime scene is often accessible only for a short period of time. During that time, all relevant evidence must be documented quickly, and even small details can prove crucial. Researchers at the Technical University of Munich ...

Machine learning & AI

New AI research improves how computers interpret the world

For artificial intelligence tools that rely on interpreting data from the real world, both speed and accuracy are critically important. Researchers at the University of Saskatchewan (USask) have developed a tool to make AI ...

Robotics

An AI-powered control system for robots with legs

Walking robots, such as quadruped robotic dogs, must be able to move safely through rough, often changing environments. Today, there are two main ways to program these walking, or legged, robots. The first is called model ...

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