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 1201–1225 of 2454 papers

TitleStatusHype
BEVStereo: Enhancing Depth Estimation in Multi-view 3D Object Detection with Dynamic Temporal StereoCode2
Self-supervised 3D Object Detection from Monocular Pseudo-LiDAR—0
On Robust Cross-View Consistency in Self-Supervised Monocular Depth EstimationCode0
3D-PL: Domain Adaptive Depth Estimation with 3D-aware Pseudo-LabelingCode1
TODE-Trans: Transparent Object Depth Estimation with TransformerCode1
SF2SE3: Clustering Scene Flow into SE(3)-Motions via Proposal and SelectionCode1
Self-distilled Feature Aggregation for Self-supervised Monocular Depth EstimationCode1
DevNet: Self-supervised Monocular Depth Learning via Density Volume ConstructionCode1
A Novel Unsupervised Domain Adaption Method for Depth-Guided Semantic Segmentation Using Coarse-to-Fine Alignment—0
A Benchmark and a Baseline for Robust Multi-view Depth EstimationCode1
StructNeRF: Neural Radiance Fields for Indoor Scenes with Structural Hints—0
BiFuse++: Self-supervised and Efficient Bi-projection Fusion for 360 Depth EstimationCode1
A comprehensive survey on recent deep learning-based methods applied to surgical data—0
LiteDepth: Digging into Fast and Accurate Depth Estimation on Mobile DevicesCode1
CLONeR: Camera-Lidar Fusion for Occupancy Grid-aided Neural Representations—0
Multi-View Reconstruction using Signed Ray Distance Functions (SRDF)—0
SimpleRecon: 3D Reconstruction Without 3D ConvolutionsCode3
Synthehicle: Multi-Vehicle Multi-Camera Tracking in Virtual CitiesCode1
SphereDepth: Panorama Depth Estimation from Spherical Domain—0
A Practical Calibration Method for RGB Micro-Grid Polarimetric CamerasCode0
Rethinking Skip Connections in Encoder-decoder Networks for Monocular Depth Estimation—0
Towards Accurate Reconstruction of 3D Scene Shape from A Single Monocular ImageCode3
Neural Camera Models—0
Uncertainty Guided Depth Fusion for Spike Camera—0
Dense Depth Distillation with Out-of-Distribution Simulated Images—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