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 701–725 of 2454 papers

TitleStatusHype
A Concise but High-performing Network for Image Guided Depth Completion in Autonomous DrivingCode1
Endo-4DGS: Endoscopic Monocular Scene Reconstruction with 4D Gaussian SplattingCode7
Range-Agnostic Multi-View Depth Estimation With Keyframe SelectionCode1
FoVA-Depth: Field-of-View Agnostic Depth Estimation for Cross-Dataset Generalization—0
InverseMatrixVT3D: An Efficient Projection Matrix-Based Approach for 3D Occupancy PredictionCode1
Icy Moon Surface Simulation and Stereo Depth Estimation for Sampling AutonomyCode0
EndoGaussian: Real-time Gaussian Splatting for Dynamic Endoscopic Scene ReconstructionCode0
Stereo-Matching Knowledge Distilled Monocular Depth Estimation Filtered by Multiple Disparity Consistency—0
MVSFormer++: Revealing the Devil in Transformer's Details for Multi-View StereoCode3
Depth Anything: Unleashing the Power of Large-Scale Unlabeled DataCode9
TDC-less Direct Time-of-Flight Imaging Using Spiking Neural Networks—0
Self-supervised Event-based Monocular Depth Estimation using Cross-modal Consistency—0
A Study on Self-Supervised Pretraining for Vision Problems in Gastrointestinal EndoscopyCode1
Surgical-DINO: Adapter Learning of Foundation Models for Depth Estimation in Endoscopic SurgeryCode2
InseRF: Text-Driven Generative Object Insertion in Neural 3D Scenes—0
RadarCam-Depth: Radar-Camera Fusion for Depth Estimation with Learned Metric ScaleCode2
NeRFmentation: NeRF-based Augmentation for Monocular Depth Estimation—0
Hi-Map: Hierarchical Factorized Radiance Field for High-Fidelity Monocular Dense Mapping—0
Denoising Vision TransformersCode3
Depth-discriminative Metric Learning for Monocular 3D Object Detection—0
iToF-flow-based High Frame Rate Depth ImagingCode0
Flexible Depth Completion for Sparse and Varying Point Densities—0
Diffusion Handles Enabling 3D Edits for Diffusion Models by Lifting Activations to 3D—0
Global and Hierarchical Geometry Consistency Priors for Few-shot NeRFs in Indoor ScenesCode1
Coherence As Texture - Passive Textureless 3D Reconstruction by Self-interference—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