What’s ahead in networking over the course of the next 12 months? It will be less a case of a rush to tech, but rather a move from what is possible to what business benefits can be unlocked.

Abstract: Kernel point convolution (KPConv) defines convolutional weights based on Euclidean distances between kernel points and input points and has shown good segmentation results on several ...

Abstract: Accurate understanding of 3D objects in complex scenes plays essential roles in the fields of intelligent transportation and autonomous driving technology. Recent deep neural networks have ...

Abstract: Point cloud denoising and normal estimation are two fundamental yet dependent problems in digital geometry processing. However, both are often independently researched, leading to ...