A point cloud is a set of points defined in a 3D metric space. Point clouds have become one
of the most significant data formats for 3D representation and are gaining increased …

In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the
top in numerous areas, namely computer vision (CV), speech recognition, and natural …

Hodykin-Huxley 方程, 感知器模型与自适应滤波器, 再到六十年代的自组织映射网络,
神经认知机, 自适应共振网络, 许多神经计算模型都发展成为信号处理, 计算机视觉 …

Accurate detection of objects in 3D point clouds is a central problem in many applications,
such as autonomous navigation, housekeeping robots, and augmented/virtual reality. To …

Recent deep networks that directly handle points in a point set, eg, PointNet, have been
state-of-the-art for supervised learning tasks on point clouds such as classification and …

We present a network architecture for processing point clouds that directly operates on a
collection of points represented as a sparse set of samples in a high-dimensional lattice …

We address semantic segmentation of road-objects from 3D LiDAR point clouds. In
particular, we wish to detect and categorize instances of interest, such as cars, pedestrians …

Many recent works on 3D object detection have focused on designing neural network
architectures that can consume point cloud data. While these approaches demonstrate …

Robust and efficient vehicle detection is an important task of environment perception of
intelligent vehicles, which directly affects the behavior decision-making and motion planning …

Robust object recognition is a crucial skill for robots operating autonomously in real world
environments. Range sensors such as LiDAR and RGBD cameras are increasingly found in …