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Fusion of neural networks, for LIDAR-based evidential road mapping

Fusion of neural networks, for LIDAR-based evidential road mapping

5 February 2021
Edouard Capellier
Franck Davoine
V. Berge-Cherfaoui
You Li
ArXivPDFHTML

Papers citing "Fusion of neural networks, for LIDAR-based evidential road mapping"

8 / 8 papers shown
Title
Progressive LiDAR Adaptation for Road Detection
Progressive LiDAR Adaptation for Road Detection
Zhe Chen
Jing Zhang
Dacheng Tao
49
153
0
02 Apr 2019
nuScenes: A multimodal dataset for autonomous driving
nuScenes: A multimodal dataset for autonomous driving
Holger Caesar
Varun Bankiti
Alex H. Lang
Sourabh Vora
Venice Erin Liong
Qiang Xu
Anush Krishnan
Yuxin Pan
G. Baldan
Oscar Beijbom
3DPC
269
5,705
0
26 Mar 2019
Logistic Regression, Neural Networks and Dempster-Shafer Theory: a New
  Perspective
Logistic Regression, Neural Networks and Dempster-Shafer Theory: a New Perspective
Thierry Denoeux
FAtt
28
118
0
05 Jul 2018
Frustum PointNets for 3D Object Detection from RGB-D Data
Frustum PointNets for 3D Object Detection from RGB-D Data
C. Qi
Wen Liu
Chenxia Wu
Hao Su
Leonidas Guibas
3DPC
144
2,259
0
22 Nov 2017
SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time
  Road-Object Segmentation from 3D LiDAR Point Cloud
SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud
Bichen Wu
Alvin Wan
Xiangyu Yue
Kurt Keutzer
3DPC
85
805
0
19 Oct 2017
PointNet: Deep Learning on Point Sets for 3D Classification and
  Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
C. Qi
Hao Su
Kaichun Mo
Leonidas Guibas
3DH
3DPC
3DV
PINN
438
14,264
0
02 Dec 2016
A Random Finite Set Approach for Dynamic Occupancy Grid Maps with
  Real-Time Application
A Random Finite Set Approach for Dynamic Occupancy Grid Maps with Real-Time Application
Dominik Nuss
Stephan Reuter
Markus Thom
Ting Yuan
Gunther Krehl
M. Maile
Axel Gern
Klaus C. J. Dietmayer
77
150
0
09 May 2016
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB
  model size
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
F. Iandola
Song Han
Matthew W. Moskewicz
Khalid Ashraf
W. Dally
Kurt Keutzer
137
7,465
0
24 Feb 2016
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