Robots and autonomous vehicles can use 3D point clouds from LiDAR sensors and camera images to perform 3D object detection. However, current techniques that combine both types of data struggle to ...

To prevent biological contamination, meat processing must ensure the right processes and equipment are in place to avoid the possibility of unwanted foreign objects entering their process at any stage ...

Here is how to perform testing of the Point-GNN algorithm using LiDAR data in the simulated environment for autonomous vehicle designs.

Robotic vision, a cornerstone of modern robotics, enables machines to interpret and respond to their surroundings effectively. This capability is achieved through image processing and object ...

Shading brings 3D forms to life, beautifully carving out the shape of objects around us. Despite the importance of shading for perception, scientists have long been puzzled about how the brain ...

Image processing with AI algorithms that can interpret images and give a diagnosis — or at least hint at one — has garnered a lot of interest. Medical customers are looking at AI accelerators.

Ritsumeikan University researchers introduce DPPFA−Net, a groundbreaking 3D object detection network melding LiDAR and image data to improve accuracy for robots and self-driving cars. Addressing ...

Early implementations of 3D imaging relied on classical 2D image processing methods. This is not efficient from a compute perspective and filters out significant amounts of useful data.