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 601–650 of 2454 papers

TitleStatusHype
Differentiable Diffusion for Dense Depth Estimation from Multi-view ImagesCode1
ChiTransformer:Towards Reliable Stereo from CuesCode1
Chitransformer: Towards Reliable Stereo From CuesCode1
GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional NetworkCode1
One Shot 3D PhotographyCode1
Generalizable Data-free Objective for Crafting Universal Adversarial PerturbationsCode1
360 Depth Estimation in the Wild -- The Depth360 Dataset and the SegFuse NetworkCode1
Geometry Uncertainty Projection Network for Monocular 3D Object DetectionCode1
On the Adversarial Robustness of Camera-based 3D Object DetectionCode1
Brain Captioning: Decoding human brain activity into images and textCode1
E-DSSR: Efficient Dynamic Surgical Scene Reconstruction with Transformer-based Stereoscopic Depth PerceptionCode1
3D-PL: Domain Adaptive Depth Estimation with 3D-aware Pseudo-LabelingCode1
Digging into contrastive learning for robust depth estimation with diffusion modelsCode1
Digging Into Self-Supervised Monocular Depth EstimationCode1
Boundary-induced and scene-aggregated network for monocular depth predictionCode1
Dyna-DM: Dynamic Object-aware Self-supervised Monocular Depth MapsCode1
Global and Hierarchical Geometry Consistency Priors for Few-shot NeRFs in Indoor ScenesCode1
Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepthCode1
End-to-End Pseudo-LiDAR for Image-Based 3D Object DetectionCode1
On the Importance of Accurate Geometry Data for Dense 3D Vision TasksCode1
Ground-aware Monocular 3D Object Detection for Autonomous DrivingCode1
GroCo: Ground Constraint for Metric Self-Supervised Monocular DepthCode1
A Practical Stereo Depth System for Smart GlassesCode1
Height and Uprightness Invariance for 3D Prediction From a Single ViewCode1
HDNet: Human Depth Estimation for Multi-Person Camera-Space LocalizationCode1
Revisiting Light Field Rendering with Deep Anti-Aliasing Neural NetworkCode1
3D Visual Illusion Depth EstimationCode1
Discrete Cosine Transform Network for Guided Depth Map Super-ResolutionCode1
Discrete Time Convolution for Fast Event-Based StereoCode1
CoDEPS: Online Continual Learning for Depth Estimation and Panoptic SegmentationCode1
P²Net: Patch-match and Plane-regularization for Unsupervised Indoor Depth EstimationCode1
Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular DepthCode1
Dual Transfer Learning for Event-based End-task Prediction via Pluggable Event to Image TranslationCode1
HR-Depth: High Resolution Self-Supervised Monocular Depth EstimationCode1
Depth Estimation from Monocular Images and Sparse radar using Deep Ordinal Regression NetworkCode1
Disparity Estimation Using a Quad-Pixel SensorCode1
Depth Estimation from Monocular Images and Sparse Radar DataCode1
Depth Estimation From Indoor Panoramas With Neural Scene RepresentationCode1
DualRefine: Self-Supervised Depth and Pose Estimation Through Iterative Epipolar Sampling and Refinement Toward EquilibriumCode1
Collaboration Helps Camera Overtake LiDAR in 3D DetectionCode1
OmniVidar: Omnidirectional Depth Estimation From Multi-Fisheye ImagesCode1
Image Masking for Robust Self-Supervised Monocular Depth EstimationCode1
Are We Ready for Vision-Centric Driving Streaming Perception? The ASAP BenchmarkCode1
Implicit Integration of Superpixel Segmentation into Fully Convolutional NetworksCode1
Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth PredictionCode1
RGB-D Indiscernible Object Counting in Underwater ScenesCode1
DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost VolumeCode1
DiverseDepth: Affine-invariant Depth Prediction Using Diverse DataCode1
Dusk Till Dawn: Self-supervised Nighttime Stereo Depth Estimation using Visual Foundation ModelsCode1
Depth estimation from 4D light field videosCode1
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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