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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1926–1950 of 2454 papers

TitleStatusHype
Monocular Depth Estimation Using Multi Scale Neural Network And Feature Fusion—0
Adjusting Bias in Long Range Stereo Matching: A semantics guided approach—0
Rain rendering for evaluating and improving robustness to bad weather—0
Approaches, Challenges, and Applications for Deep Visual Odometry: Toward to Complicated and Emerging Areas—0
DESC: Domain Adaptation for Depth Estimation via Semantic Consistency—0
Depth Completion via Inductive Fusion of Planar LIDAR and Monocular Camera—0
GPR-based Subsurface Object Detection and Reconstruction Using Random Motion and DepthNet—0
Exploring the Impacts from Datasets to Monocular Depth Estimation (MDE) Models with MineNavi—0
Balanced Depth Completion between Dense Depth Inference and Sparse Range Measurements via KISS-GP—0
Fast and Accurate Optical Flow based Depth Map Estimation from Light Fields—0
SynDistNet: Self-Supervised Monocular Fisheye Camera Distance Estimation Synergized with Semantic Segmentation for Autonomous Driving—0
Shape Consistent 2D Keypoint Estimation under Domain Shift—0
MSDPN: Monocular Depth Prediction with Partial Laser Observation using Multi-stage Neural Networks—0
Du²Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels—0
Joint 3D Layout and Depth Prediction from a Single Indoor Panorama Image—0
Pixel-Pair Occlusion Relationship Map (P2ORM): Formulation, Inference & Application—0
Disambiguating Monocular Depth Estimation with a Single Transient—0
HMOR: Hierarchical Multi-Person Ordinal Relations for Monocular Multi-Person 3D Pose Estimation—0
S³Net: Semantic-Aware Self-supervised Depth Estimation with Monocular Videos and Synthetic Data—0
CLIFFNet for Monocular Depth Estimation with Hierarchical Embedding Loss—0
Real-Time Uncertainty Estimation in Computer Vision via Uncertainty-Aware Distribution Distillation—0
On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures—0
S^3Net: Semantic-Aware Self-supervised Depth Estimation with Monocular Videos and Synthetic Data—0
Adaptive LiDAR Sampling and Depth Completion using Ensemble VarianceCode0
Robust Vision Using Retro Reflective Markers for Remote Handling in ITER—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