SOTAVerified

Depth Estimation

Depth Estimation is the task of measuring the distance of each pixel relative to the camera. Depth is extracted from either monocular (single) or stereo (multiple views of a scene) images. Traditional methods use multi-view geometry to find the relationship between the images. Newer methods can directly estimate depth by minimizing the regression loss, or by learning to generate a novel view from a sequence. The most popular benchmarks are KITTI and NYUv2. Models are typically evaluated according to a RMS metric.

Source: DIODE: A Dense Indoor and Outdoor DEpth Dataset

Papers

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Showing 2101–2150 of 2454 papers

TitleStatusHype
Learning Common Representation from RGB and Depth Images—0
Learning Deeply Supervised Good Features to Match for Dense Monocular Reconstruction—0
Learning depth from monocular video sequences—0
Learning Depth from Monocular Videos Using Synthetic Data: A Temporally-Consistent Domain Adaptation Approach—0
Learning Depth from Single Images with Deep Neural Network Embedding Focal Length—0
Learning Depth With Very Sparse Supervision—0
Learning Depth via Leveraging Semantics: Self-supervised Monocular Depth Estimation with Both Implicit and Explicit Semantic Guidance—0
Learning Feature Decomposition for Domain Adaptive Monocular Depth Estimation—0
Learning Good Features to Transfer Across Tasks and Domains—0
Learning Inverse Laplacian Pyramid for Progressive Depth Completion—0
Learning Monocular Depth Estimation via Selective Distillation of Stereo Knowledge—0
Learning Monocular Depth from Events via Egomotion Compensation—0
Learning Monocular Depth in Dynamic Environment via Context-aware Temporal Attention—0
Learning Optical Flow, Depth, and Scene Flow without Real-World Labels—0
Learning Ordinal Relationships for Mid-Level Vision—0
Learning Phase Mask for Privacy-Preserving Passive Depth Estimation—0
Learning Robust Multi-Scale Representation for Neural Radiance Fields from Unposed Images—0
Learning Scene Structure Guidance via Cross-Task Knowledge Transfer for Single Depth Super-Resolution—0
Learning Semantic Segmentation from Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach—0
Learning Single-Image Depth from Videos using Quality Assessment Networks—0
Learning Structured Gaussians to Approximate Deep Ensembles—0
Learning structure-from-motion from motion—0
Learning Subjective Time-Series Data via Utopia Label Distribution Approximation—0
Learning Temporally Consistent Video Depth from Video Diffusion Priors—0
Learning the Depths of Moving People by Watching Frozen People—0
Real-Time Uncertainty Estimation in Computer Vision via Uncertainty-Aware Distribution Distillation—0
Learning to Adapt CLIP for Few-Shot Monocular Depth Estimation—0
Learning to Autofocus—0
Learning to compose 6-DoF omnidirectional videos using multi-sphere images—0
Learning to Efficiently Adapt Foundation Models for Self-Supervised Endoscopic 3D Scene Reconstruction from Any Cameras—0
Learning to Estimate Two Dense Depths from LiDAR and Event Data—0
Learning to Infer the Depth Map of a Hand from its Color Image—0
Learning to Project for Cross-Task Knowledge Distillation—0
Learning to Reconstruct and Understand Indoor Scenes from Sparse Views—0
Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift—0
Learning Visual Representation from Human Interactions—0
Learn to Adapt for Monocular Depth Estimation—0
Learn to Teach: Sample-Efficient Privileged Learning for Humanoid Locomotion over Diverse Terrains—0
LED2-Net: Monocular 360deg Layout Estimation via Differentiable Depth Rendering—0
Leveraging a realistic synthetic database to learn Shape-from-Shading for estimating the colon depth in colonoscopy images—0
Recovering Detail in 3D Shapes Using Disparity Maps—0
Leveraging Near-Field Lighting for Monocular Depth Estimation from Endoscopy Videos—0
Leveraging Stable Diffusion for Monocular Depth Estimation via Image Semantic Encoding—0
Leveraging the Third Dimension in Contrastive Learning—0
LiFCal: Online Light Field Camera Calibration via Bundle Adjustment—0
Lift-Attend-Splat: Bird's-eye-view camera-lidar fusion using transformers—0
Lifting GIS Maps into Strong Geometric Context for Scene Understanding—0
LightDepth: Single-View Depth Self-Supervision from Illumination Decline—0
Light field Rectification based on relative pose estimation—0
Light Field Stitching for Extended Synthetic Aperture—0
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All datasetsStanford2D3D PanoramicNYU-Depth V2DCMeBDthequeScanNetV2Cityscapes testDIODEKITTI 2015Mars DTM EstimationScanNet4D Light Field DatasetKITTI Eigen split

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1OmniDepthRMSE0.62—Unverified
2SphereDepthRMSE0.45—Unverified
3Jin et al.RMSE0.42—Unverified
4BiFuse with fusionRMSE0.41—Unverified
5HoHoNet (ResNet-101)RMSE0.38—Unverified
6PanoDepthRMSE0.37—Unverified
7BiFuse++RMSE0.37—Unverified
8UniFuse with fusionRMSE0.37—Unverified
9DisConvRMSE0.37—Unverified
10SliceNetRMSE0.37—Unverified
#ModelMetricClaimedVerifiedStatus
1A2JmAP8.61—Unverified
2PAD-NetRMS0.79—Unverified
3MS-CRFRMS0.59—Unverified
4DORNRMS0.51—Unverified
5FreeformRMS0.43—Unverified
6Optimized, freeformRMS0.43—Unverified
7VNLRMS0.42—Unverified
8BTSRMS0.41—Unverified
9TransDepth (AGD+ ViT)RMS0.37—Unverified
10AdaBinsRMS0.36—Unverified
#ModelMetricClaimedVerifiedStatus
1T2NetAbs Rel0.35—Unverified
2MIDASAbs Rel0.31—Unverified
3Bhattacharjee et al.Abs Rel0.25—Unverified
#ModelMetricClaimedVerifiedStatus
1T2NetAbs Rel0.49—Unverified
2MIDASAbs Rel0.42—Unverified
3Bhattacharjee et al.Abs Rel0.38—Unverified
#ModelMetricClaimedVerifiedStatus
1LeReSabsolute relative error0.1—Unverified
2DELTASabsolute relative error0.09—Unverified
3Distill Any Depthabsolute relative error0.04—Unverified
#ModelMetricClaimedVerifiedStatus
1SDC-DepthRMSE6.92—Unverified
2SwinMTLRMSE6.35—Unverified
#ModelMetricClaimedVerifiedStatus
1AIP-BrownDelta < 1.250.36—Unverified
2LeResDelta < 1.250.23—Unverified
#ModelMetricClaimedVerifiedStatus
1H-Net (Ours)Absolute relative error (AbsRel)0.09—Unverified
2H-Net (Ours) Full EigenAbsolute relative error (AbsRel)0.08—Unverified
#ModelMetricClaimedVerifiedStatus
1GLPDepthDelta < 1.250.43—Unverified
2SRDINET (Model A)Delta < 1.250.4—Unverified
#ModelMetricClaimedVerifiedStatus
1Atlas (finetuned)RMSE0.17—Unverified
2Atlas (plain)RMSE0.17—Unverified
#ModelMetricClaimedVerifiedStatus
1LFattNetBadPix(0.01)17.23—Unverified
#ModelMetricClaimedVerifiedStatus
1LightDepthNumber of parameters (M)42.6—Unverified
#ModelMetricClaimedVerifiedStatus
1UniFuseAbs Rel0.11—Unverified
#ModelMetricClaimedVerifiedStatus
1X-TC (Cross-Task Consistency)L1 error1.63—Unverified