Search Results for author:
Found 5 papers, 2 papers with code
Fooling LiDAR Perception via Adversarial Trajectory Perturbation
LiDAR point clouds collected from a moving vehicle are functions of its trajectories, because the sensor motion needs to be compensated to avoid distortions.
AETree: Areal Spatial Data Generation
Areal spatial data represent not only geographical locations but also sizes and shapes of physical objects such as buildings in a city.
Airborne LiDAR Point Cloud Classification with Graph Attention Convolution Neural Network
To achieve point cloud classification, previous studies proposed point cloud deep learning models that can directly process raw point clouds based on PointNet-like architectures.
Density-Aware Convolutional Networks with Context Encoding for Airborne LiDAR Point Cloud Classification
Based on this convolution module, we further developed a multi-scale fully convolutional neural network with downsampling and upsampling blocks to enable hierarchical point feature learning.
Directionally Constrained Fully Convolutional Neural Network For Airborne Lidar Point Cloud Classification
In this paper, we proposed a directionally constrained fully convolutional neural network (D-FCN) that can take the original 3D coordinates and LiDAR intensity as input; thus, it can directly apply to unstructured 3D point clouds for semantic labeling.
