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 1701–1750 of 2454 papers

TitleStatusHype
Exploring Depth Contribution for Camouflaged Object Detection—0
DepthCues: Evaluating Monocular Depth Perception in Large Vision Models—0
Depth-discriminative Metric Learning for Monocular 3D Object Detection—0
Depth Estimation Algorithm Based on Transformer-Encoder and Feature Fusion—0
Depth Estimation Analysis of Orthogonally Divergent Fisheye Cameras with Distortion Removal—0
Depth Estimation and Image Restoration by Deep Learning from Defocused Images—0
Depth Estimation Based on 3D Gaussian Splatting Siamese Defocus—0
Depth Estimation from a Single Optical Encoded Image using a Learned Colored-Coded Aperture—0
Depth Estimation From Camera Image and mmWave Radar Point Cloud—0
Depth Estimation from Single Image using Sparse Representations—0
Depth Estimation from Single-shot Monocular Endoscope Image Using Image Domain Adaptation And Edge-Aware Depth Estimation—0
Depth Estimation fusing Image and Radar Measurements with Uncertain Directions—0
Depth Estimation maps of lidar and stereo images—0
Depth Estimation Matters Most: Improving Per-Object Depth Estimation for Monocular 3D Detection and Tracking—0
Depth estimation of endoscopy using sim-to-real transfer—0
Depth Estimation on Underwater Omni-directional Images Using a Deep Neural Network—0
Depth Estimation Through a Generative Model of Light Field Synthesis—0
Depth Estimation using Modified Cost Function for Occlusion Handling—0
Depth estimation using structured light flow -- analysis of projected pattern flow on an object's surface --—0
Depth Estimation Using Structured Light Flow -- Analysis of Projected Pattern Flow on an Object's Surface—0
Depth Estimation using Weighted-loss and Transfer Learning—0
Depth Estimation with Simplified Transformer—0
Depth Extraction from Videos Using Geometric Context and Occlusion Boundaries—0
Depth Extraction from Video Using Non-parametric Sampling—0
DepthFake: a depth-based strategy for detecting Deepfake videos—0
Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions—0
Depth from Monocular Images using a Semi-Parallel Deep Neural Network (SPDNN) Hybrid Architecture—0
Depth From Shading, Defocus, and Correspondence Using Light-Field Angular Coherence—0
Depth-Guided Semi-Supervised Instance Segmentation—0
Depth Insight -- Contribution of Different Features to Indoor Single-image Depth Estimation—0
DepthInSpace: Exploitation and Fusion of Multiple Video Frames for Structured-Light Depth Estimation—0
Depth Is All You Need for Monocular 3D Detection—0
DepthMamba with Adaptive Fusion—0
Depth Map Estimation for Free-Viewpoint Television—0
DepthNet Nano: A Highly Compact Self-Normalizing Neural Network for Monocular Depth Estimation—0
Depth on Demand: Streaming Dense Depth from a Low Frame Rate Active Sensor—0
Depth Perspective-aware Multiple Object Tracking—0
DepthP+P: Metric Accurate Monocular Depth Estimation using Planar and Parallax—0
Depth Priors in Removal Neural Radiance Fields—0
Depth Reconstruction with Neural Signed Distance Fields in Structured Light Systems—0
Depth Refinement for Improved Stereo Reconstruction—0
Depth-Relative Self Attention for Monocular Depth Estimation—0
Depth Restoration of Hand-Held Transparent Objects for Human-to-Robot Handover—0
Dynamic Depth-Supervised NeRF for Multi-View RGB-D Operating Room Images—0
DepthTransfer: Depth Extraction from Video Using Non-parametric Sampling—0
DESC: Domain Adaptation for Depth Estimation via Semantic Consistency—0
DesNet: Decomposed Scale-Consistent Network for Unsupervised Depth Completion—0
Detail Preserving Depth Estimation from a Single Image Using Attention Guided Networks—0
Detecting Car Speed using Object Detection and Depth Estimation: A Deep Learning Framework—0
Detecting Deficient Coverage in Colonoscopies—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