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CReaM: Condensed Real-time Models for Depth Prediction using
  Convolutional Neural Networks

CReaM: Condensed Real-time Models for Depth Prediction using Convolutional Neural Networks

24 July 2018
Andrew Spek
Thanuja Dharmasiri
Tom Drummond
ArXivPDFHTML

Papers citing "CReaM: Condensed Real-time Models for Depth Prediction using Convolutional Neural Networks"

5 / 5 papers shown
Title
Lightweight Monocular Depth Estimation with an Edge Guided Network
Lightweight Monocular Depth Estimation with an Edge Guided Network
Xingshuai Dong
Matthew A. Garratt
S. Anavatti
H. Abbass
Junyu Dong
MDE
25
2
0
29 Sep 2022
EMPNet: Neural Localisation and Mapping Using Embedded Memory Points
EMPNet: Neural Localisation and Mapping Using Embedded Memory Points
Gil Avraham
Yan Zuo
Thanuja Dharmasiri
Tom Drummond
21
17
0
31 Jul 2019
Double Refinement Network for Efficient Indoor Monocular Depth
  Estimation
Double Refinement Network for Efficient Indoor Monocular Depth Estimation
N. Durasov
Mikhail Romanov
Valeriya Bubnova
P. Bogomolov
Anton Konushin
MDE
3DV
25
10
0
20 Nov 2018
ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D
  Cameras
ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras
Raul Mur-Artal
Juan D. Tardós
204
5,375
0
20 Oct 2016
ENet: A Deep Neural Network Architecture for Real-Time Semantic
  Segmentation
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
Adam Paszke
Abhishek Chaurasia
Sangpil Kim
Eugenio Culurciello
SSeg
235
2,056
0
07 Jun 2016
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