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URCDC-Depth: Uncertainty Rectified Cross-Distillation with CutFlip for
  Monocular Depth Estimation

URCDC-Depth: Uncertainty Rectified Cross-Distillation with CutFlip for Monocular Depth Estimation

16 February 2023
Shuwei Shao
Z. Pei
Weihai Chen
Ran Li
Zhong Liu
Zhengguo Li
    ViT
    UQCV
ArXivPDFHTML

Papers citing "URCDC-Depth: Uncertainty Rectified Cross-Distillation with CutFlip for Monocular Depth Estimation"

9 / 9 papers shown
Title
VGLD: Visually-Guided Linguistic Disambiguation for Monocular Depth Scale Recovery
VGLD: Visually-Guided Linguistic Disambiguation for Monocular Depth Scale Recovery
Bojin Wu
Jing Chen
MDE
46
0
0
05 May 2025
MonoTher-Depth: Enhancing Thermal Depth Estimation via Confidence-Aware Distillation
MonoTher-Depth: Enhancing Thermal Depth Estimation via Confidence-Aware Distillation
Xingxing Zuo
Nikhil Ranganathan
Connor T. Lee
Georgia Gkioxari
Soon-Jo Chung
VLM
58
1
0
21 Apr 2025
Vision-Language Embodiment for Monocular Depth Estimation
Vision-Language Embodiment for Monocular Depth Estimation
Jinchang Zhang
Guoyu Lu
VLM
MDE
50
0
0
18 Mar 2025
MAMo: Leveraging Memory and Attention for Monocular Video Depth Estimation
MAMo: Leveraging Memory and Attention for Monocular Video Depth Estimation
R. Yasarla
H. Cai
Jisoo Jeong
Y. Shi
Risheek Garrepalli
Fatih Porikli
MDE
63
16
0
17 Jan 2025
MetricDepth: Enhancing Monocular Depth Estimation with Deep Metric Learning
MetricDepth: Enhancing Monocular Depth Estimation with Deep Metric Learning
Chunpu Liu
Guanglei Yang
Wangmeng Zuo
Tianyi Zan
MDE
51
0
0
31 Dec 2024
Deep Ordinal Regression Network for Monocular Depth Estimation
Deep Ordinal Regression Network for Monocular Depth Estimation
Huan Fu
Mingming Gong
Chaohui Wang
Kayhan Batmanghelich
Dacheng Tao
MDE
182
1,707
0
06 Jun 2018
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
270
5,660
0
05 Dec 2016
Densely Connected Convolutional Networks
Densely Connected Convolutional Networks
Gao Huang
Zhuang Liu
L. V. D. van der Maaten
Kilian Q. Weinberger
PINN
3DV
252
36,362
0
25 Aug 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
285
9,136
0
06 Jun 2015
1