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Monocular Depth Estimation On Nyu Depth V2

Metrics

Delta u003c 1.25
Delta u003c 1.25^2
Delta u003c 1.25^3
RMSE
absolute relative error
log 10

Results

Performance results of various models on this benchmark

Paper TitleRepository
Eigen et al.---0.641--Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture
Li et al.---0.635--A Two-Streamed Network for Estimating Fine-Scaled Depth Maps from Single RGB Images-
SOM---0.604--Structure-Attentioned Memory Network for Monocular Depth Estimation-
PGT (Swin-T)---0.59--Prompt Guided Transformer for Multi-Task Dense Prediction
Xu et al.---0.586--Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation
DeepLabV3+ (F10)---0.575--Single Image Depth Estimation Trained via Depth from Defocus Cues
Index Network---0.565--Index Network
Multi-Task Light-Weight-RefineNet---0.565--Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations
PGT (Swin-S)---0.5468--Prompt Guided Transformer for Multi-Task Dense Prediction
RelativeDepth---0.538--Monocular Depth Estimation Using Relative Depth Maps-
ProbMonoDepth---0.536--Generating and Exploiting Probabilistic Monocular Depth Estimates
SC-DepthV20.8200.956-0.5320.1380.059Auto-Rectify Network for Unsupervised Indoor Depth Estimation
SENet-154---0.530--Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps with Accurate Object Boundaries
FastDenseNas-arch1---0.526--Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
FastDenseNas-arch2---0.525--Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
FastDenseNas-arch0---0.523--Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
InvPT---0.5183--InvPT: Inverted Pyramid Multi-task Transformer for Dense Scene Understanding
SARPN---0.514--Structure-Aware Residual Pyramid Network for Monocular Depth Estimation
DORN---0.509--Deep Ordinal Regression Network for Monocular Depth Estimation
PAP-Depth---0.497--Pattern-Affinitive Propagation across Depth, Surface Normal and Semantic Segmentation-
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Monocular Depth Estimation On Nyu Depth V2 | SOTA | HyperAI