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Monocular Depth Estimation On Kitti Eigen 1

Metrics

Delta u003c 1.25
Delta u003c 1.25^2
Delta u003c 1.25^3
Mono
RMSE
RMSE log
Sq Rel
absolute relative error

Results

Performance results of various models on this benchmark

Paper TitleRepository
Struct2Depth M-------0.1412Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics-
Monodepth S-------0.133Unsupervised Monocular Depth Estimation with Left-Right Consistency
Monodepth2 M-------0.115Digging Into Self-Supervised Monocular Depth Estimation-
Dyna-DM0.8710.9590.982-4.6980.1920.7850.115Dyna-DM: Dynamic Object-aware Self-supervised Monocular Depth Maps
pc4consistentdepth-------0.113Pose Constraints for Consistent Self-supervised Monocular Depth and Ego-motion
Occlusion_mask_640x192-------0.113Improving Self-Supervised Single View Depth Estimation by Masking Occlusion
SuperDepth S-------0.112SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation-
DCPI-Depth (M+832x256+SC-V3)----4.496-0.6790.109DCPI-Depth: Explicitly Infusing Dense Correspondence Prior to Unsupervised Monocular Depth Estimation-
HR-Depth-M-640x192-------0.109HR-Depth: High Resolution Self-Supervised Monocular Depth Estimation
SharinGAN0.8640.9540.981-3.770.190.6730.109SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation
G2S (MD2-M-R18-pp-640 x 192)-------0.109Multimodal Scale Consistency and Awareness for Monocular Self-Supervised Depth Estimation
Monodepth2 S-------0.109Digging Into Self-Supervised Monocular Depth Estimation-
PackNet-SfM M-------0.1073D Packing for Self-Supervised Monocular Depth Estimation
CamLessMonoDepth (V2)-640x192-------0.106CamLessMonoDepth: Monocular Depth Estimation with Unknown Camera Parameters-
Monodepth2 MS-------0.106Digging Into Self-Supervised Monocular Depth Estimation-
CamLessMonoDepth (V1)-640x192-------0.105CamLessMonoDepth: Monocular Depth Estimation with Unknown Camera Parameters-
VTDepthB2 (monocular supervision)0.8930.9640.983-4.5300.1820.7620.105Exploring Efficiency of Vision Transformers for Self-Supervised Monocular Depth Estimation-
GCNDepth----4.4940.1810.7200.104GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional Network
Lite-HR-Depth-T-1280x384-------0.104HR-Depth: High Resolution Self-Supervised Monocular Depth Estimation
MonoFormer---O---0.104Deep Digging into the Generalization of Self-Supervised Monocular Depth Estimation
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Monocular Depth Estimation On Kitti Eigen 1 | SOTA | HyperAI