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 2051–2075 of 2454 papers

TitleStatusHype
RayMVSNet++: Learning Ray-based 1D Implicit Fields for Accurate Multi-View Stereo—0
RDG-GS: Relative Depth Guidance with Gaussian Splatting for Real-time Sparse-View 3D Rendering—0
RealCam-I2V: Real-World Image-to-Video Generation with Interactive Complex Camera Control—0
RealMonoDepth: Self-Supervised Monocular Depth Estimation for General Scenes—0
Real-time Dynamic Object Detection for Autonomous Driving using Prior 3D-Maps—0
Real-Time Hybrid Mapping of Populated Indoor Scenes using a Low-Cost Monocular UAV—0
Real-time Joint Object Detection and Semantic Segmentation Network for Automated Driving—0
Real-time Monocular Depth Estimation with Sparse Supervision on Mobile—0
Real-time Multi-view Omnidirectional Depth Estimation System for Robots and Autonomous Driving on Real Scenes—0
Real-time Surgical Environment Enhancement for Robot-Assisted Minimally Invasive Surgery Based on Super-Resolution—0
ReCoRe: Regularized Contrastive Representation Learning of World Model—0
Rectification-specific Supervision and Constrained Estimator for Online Stereo Rectification—0
Recurrent Neural Network for Learning DenseDepth and Ego-Motion from Video—0
Recurrent Neural Network for (Un-)Supervised Learning of Monocular Video Visual Odometry and Depth—0
Recurrent Structure Attention Guidance for Depth Super-Resolution—0
RefGaussian: Disentangling Reflections from 3D Gaussian Splatting for Realistic Rendering—0
Refine and Distill: Exploiting Cycle-Inconsistency and Knowledge Distillation for Unsupervised Monocular Depth Estimation—0
Reflection Separation and Deblurring of Plenoptic Images—0
Region-aware Distribution Contrast: A Novel Approach to Multi-Task Partially Supervised Learning—0
Reinforcement Learning-Based Monocular Vision Approach for Autonomous UAV Landing—0
Relative Depth Estimation as a Ranking Problem—0
ResearchDoom and CocoDoom: Learning Computer Vision with Games—0
Residual-Guided Learning Representation for Self-Supervised Monocular Depth Estimation—0
Rethinking Encoder-Decoder Flow Through Shared Structures—0
Rethinking Monocular Depth Estimation with Adversarial Training—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