Fast Semi-dense Depth Map Estimation-Reference-Cited by-同舟云学术

Fast Semi-dense Depth Map Estimation

Published:2018-06-06 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2018 ACM Workshop on Multimedia for Real Estate Tech
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Author:

Makarov Ilya¹,Korinevskaya Alisa¹,Aliev Vladimir¹

Affiliation:

1. National Research University Higher School of Economics, Moscow, Russian Fed.

Funder

This work has been funded by the Russian Academic Excellence Project '5-100'.

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3210499.3210529

Reference22 articles.

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2. Cesar Cadena Anthony R Dick and Ian D Reid. 2016. Multi-modal Auto-Encoders as Joint Estimators for Robotics Scene Understanding Robotics: Science and Systems. RSS Foundation Pennsylvania USA. Cesar Cadena Anthony R Dick and Ian D Reid. 2016. Multi-modal Auto-Encoders as Joint Estimators for Robotics Scene Understanding Robotics: Science and Systems. RSS Foundation Pennsylvania USA.

3. Matterport3D: Learning from RGB-D Data in Indoor Environments

4. Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture

5. Extended Guided Filtering for Depth Map Upsampling

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