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Found 8 papers, 0 papers with code
Spatial Layout Consistency for 3D Semantic Segmentation
We introduce a novel deep convolutional neural network (DCNN) technique for achieving voxel-based semantic segmentation of the ALTM's point clouds.
NSANet: Noise Seeking Attention Network
LiDAR (Light Detection and Ranging) technology has remained popular in capturing natural and built environments for numerous applications.
Deep Convolutional Neural Network for Plume Rise Measurements in Industrial Environments
Smokestack Plume Rise (PR) is the constant height at which the PC is carried downwind as its momentum dissipates and the PC and the ambient temperatures equalize.
TPE-Net: Track Point Extraction and Association Network for Rail Path Proposal Generation
To estimate the risk, the control system must identify topological information of all the rail routes ahead on which the train can possibly move, especially within merging or diverging rails.
Human Vision Based 3D Point Cloud Semantic Segmentation of Large-Scale Outdoor Scene
This paper proposes EyeNet, a novel semantic segmentation network for point clouds that addresses the critical yet often overlooked parameter of coverage area size.
HDPV-SLAM: Hybrid Depth-augmented Panoramic Visual SLAM for Mobile Mapping System with Tilted LiDAR and Panoramic Visual Camera
During a phase of feature tracking, this hybrid depth association module aims to maximize the use of more accurate depth information between the triangulated depth with visual features tracked and the deep learning-based corrected depth.
Boundary Regularized Building Footprint Extraction From Satellite Images Using Deep Neural Network
The proposed deep learning method consists of a two-stage object detection network to produce region of interest (RoI) features and a building boundary extraction network using graph models to learn geometric information of the polygon shapes.
BOUNDARY REGULARIZED BUILDING FOOTPRINT EXTRACTION FROM SATELLITE IMAGES USING DEEP NEURAL NETWORKS
The proposed deep learning method consists of a two-stage object detection network to produce region of interest (RoI) features and a building boundary extraction network using graph models to learn geometric information of the polygon shapes.
