ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1703.04699
  4. Cited By
A fully end-to-end deep learning approach for real-time simultaneous 3D
  reconstruction and material recognition

A fully end-to-end deep learning approach for real-time simultaneous 3D reconstruction and material recognition

14 March 2017
Cheng Zhao
Li Sun
Rustam Stolkin
    3DV
ArXivPDFHTML

Papers citing "A fully end-to-end deep learning approach for real-time simultaneous 3D reconstruction and material recognition"

3 / 3 papers shown
Title
A Taxonomy of Semantic Information in Robot-Assisted Disaster Response
A Taxonomy of Semantic Information in Robot-Assisted Disaster Response
Tianshu Ruan
Hao Wang
Rustam Stolkin
Manolis Chiou
21
1
0
30 Sep 2022
Recurrent-OctoMap: Learning State-based Map Refinement for Long-Term
  Semantic Mapping with 3D-Lidar Data
Recurrent-OctoMap: Learning State-based Map Refinement for Long-Term Semantic Mapping with 3D-Lidar Data
Li Sun
Zhi Yan
Anestis Zaganidis
Cheng Zhao
T. Duckett
3DPC
27
72
0
02 Jul 2018
Context-Aware Mixed Reality: A Framework for Ubiquitous Interaction
Context-Aware Mixed Reality: A Framework for Ubiquitous Interaction
Long Chen
Wen Tang
N. John
T. Wan
J. J. Zhang
25
16
0
14 Mar 2018
1