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SOTA
Depth Estimation
Depth Estimation On Cityscapes Test
Depth Estimation On Cityscapes Test
Metrics
RMSE
Results
Performance results of various models on this benchmark
Columns
Model Name
RMSE
Paper Title
SDC-Depth
6.917
SDC-Depth: Semantic Divide-and-Conquer Network for Monocular Depth Estimation
SwinMTL
6.352
SwinMTL: A Shared Architecture for Simultaneous Depth Estimation and Semantic Segmentation from Monocular Camera Images
0 of 2 row(s) selected.
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Next
HyperAI
HyperAI
Home
Console
Docs
News
Papers
Tutorials
Datasets
Wiki
SOTA
LLM Models
GPU Leaderboard
Events
Search
About
Terms of Service
Privacy Policy
English
HyperAI
HyperAI
Toggle Sidebar
Search the site…
⌘
K
Command Palette
Search for a command to run...
Console
Home
SOTA
Depth Estimation
Depth Estimation On Cityscapes Test
Depth Estimation On Cityscapes Test
Metrics
RMSE
Results
Performance results of various models on this benchmark
Columns
Model Name
RMSE
Paper Title
SDC-Depth
6.917
SDC-Depth: Semantic Divide-and-Conquer Network for Monocular Depth Estimation
SwinMTL
6.352
SwinMTL: A Shared Architecture for Simultaneous Depth Estimation and Semantic Segmentation from Monocular Camera Images
0 of 2 row(s) selected.
Previous
Next
Depth Estimation On Cityscapes Test | SOTA | HyperAI