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. 2006.02068
  4. Cited By
PLG-IN: Pluggable Geometric Consistency Loss with Wasserstein Distance
  in Monocular Depth Estimation

PLG-IN: Pluggable Geometric Consistency Loss with Wasserstein Distance in Monocular Depth Estimation

3 June 2020
Noriaki Hirose
Satoshi Koide
Keisuke Kawano
R. Kondo
ArXivPDFHTML

Papers citing "PLG-IN: Pluggable Geometric Consistency Loss with Wasserstein Distance in Monocular Depth Estimation"

3 / 3 papers shown
Title
Unsupervised Simultaneous Learning for Camera Re-Localization and Depth
  Estimation from Video
Unsupervised Simultaneous Learning for Camera Re-Localization and Depth Estimation from Video
S. Taguchi
Noriaki Hirose
SSL
MDE
23
1
0
24 Mar 2022
Depth360: Self-supervised Learning for Monocular Depth Estimation using
  Learnable Camera Distortion Model
Depth360: Self-supervised Learning for Monocular Depth Estimation using Learnable Camera Distortion Model
Noriaki Hirose
Kosuke Tahara
MDE
31
4
0
20 Oct 2021
Variational Monocular Depth Estimation for Reliability Prediction
Variational Monocular Depth Estimation for Reliability Prediction
Noriaki Hirose
S. Taguchi
Keisuke Kawano
Satoshi Koide
MDE
34
4
0
24 Nov 2020
1