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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

LU-Net: An Efficient Network for 3D LiDAR Point Cloud Semantic Segmentation Based on End-to-End-Learned 3D Features and U-Net

30 August 2019
P. Biasutti
Vincent Lepetit
Jean-François Aujol
Mathieu Brédif
Aurélie Bugeau
    3DV
    SSeg
    3DPC
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Papers citing "LU-Net: An Efficient Network for 3D LiDAR Point Cloud Semantic Segmentation Based on End-to-End-Learned 3D Features and U-Net"

3 / 3 papers shown
Title
LiMoSeg: Real-time Bird's Eye View based LiDAR Motion Segmentation
LiMoSeg: Real-time Bird's Eye View based LiDAR Motion Segmentation
S. Mohapatra
Mona Hodaei
S. Yogamani
Stefan Milz
H. Gotzig
Martin Simon
Hazem Rashed
Patrick Mäder
17
27
0
08 Nov 2021
FPS-Net: A Convolutional Fusion Network for Large-Scale LiDAR Point
  Cloud Segmentation
FPS-Net: A Convolutional Fusion Network for Large-Scale LiDAR Point Cloud Segmentation
Aoran Xiao
Xiaofei Yang
Shijian Lu
Dayan Guan
Jiaxing Huang
3DPC
27
48
0
01 Mar 2021
Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey and
  Experimental Study
Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey and Experimental Study
Biao Gao
Yancheng Pan
Chengkun Li
Sibo Geng
Huijing Zhao
3DPC
19
25
0
08 Jun 2020
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