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LU-Net: An Efficient Network for 3D LiDAR Point Cloud Semantic Segmentation Based on End-to-End-Learned 3D Features and U-Net.
Pierre Biasutti
Vincent Lepetit
Jean-François Aujol
Mathieu Brédif
Aurélie Bugeau
Published in:
CoRR (2019)
Keyphrases
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end to end
point cloud
congestion control
semantic segmentation
structure from motion
point sets
laser scanner
feature vectors
point cloud data
image features
conditional random fields
single image
high resolution
superpixels
pairwise
image sequences
three dimensional
machine learning