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 1376–1400 of 2454 papers

TitleStatusHype
CroMo: Cross-Modal Learning for Monocular Depth Estimation—0
Learning Occlusion-Aware Coarse-to-Fine Depth Map for Self-supervised Monocular Depth EstimationCode1
DiffPoseNet: Direct Differentiable Camera Pose Estimation—0
Depth Completion using Geometry-Aware EmbeddingCode1
Depth Estimation by Combining Binocular Stereo and Monocular Structured-LightCode1
Distortion-Tolerant Monocular Depth Estimation On Omnidirectional Images Using Dual-cubemap—0
Semi-Supervised Learning with Mutual Distillation for Monocular Depth Estimation—0
PanoFormer: Panorama Transformer for Indoor 360 Depth EstimationCode1
Depth-aware Neural Style Transfer using Instance Normalization—0
MonoJSG: Joint Semantic and Geometric Cost Volume for Monocular 3D Object DetectionCode1
From 2D to 3D: Re-thinking Benchmarking of Monocular Depth Prediction—0
Unsupervised Learning Based Focal Stack Camera Depth Estimation—0
SelfTune: Metrically Scaled Monocular Depth Estimation through Self-Supervised Learning—0
Monocular Depth Distribution Alignment with Low ComputationCode1
A high-precision self-supervised monocular visual odometry in foggy weather based on robust cycled generative adversarial networks and multi-task learning aided depth estimation—0
ChiTransformer:Towards Reliable Stereo from CuesCode1
Joint Learning of Salient Object Detection, Depth Estimation and Contour ExtractionCode1
Lightweight Monocular Depth Estimation through Guided DecodingCode1
Point Spread Function Estimation of DefocusCode0
3D endoscopic depth estimation using 3D surface-aware constraints—0
Time-to-Label: Temporal Consistency for Self-Supervised Monocular 3D Object Detection—0
Real-Time Hybrid Mapping of Populated Indoor Scenes using a Low-Cost Monocular UAV—0
Pseudo-Stereo for Monocular 3D Object Detection in Autonomous DrivingCode1
PatchMVSNet: Patch-wise Unsupervised Multi-View Stereo for Weakly-Textured Surface Reconstruction—0
Fast Neural Architecture Search for Lightweight Dense Prediction Networks—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