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
LiDAR Meta Depth CompletionCode1
3D-PL: Domain Adaptive Depth Estimation with 3D-aware Pseudo-LabelingCode1
Boundary-induced and scene-aggregated network for monocular depth predictionCode1
360 Depth Estimation in the Wild -- The Depth360 Dataset and the SegFuse NetworkCode1
Finite Scalar Quantization: VQ-VAE Made SimpleCode1
Flare-Free Vision: Empowering Uformer with Depth InsightsCode1
FreDSNet: Joint Monocular Depth and Semantic Segmentation with Fast Fourier ConvolutionsCode1
Depth Estimation from Monocular Images and Sparse radar using Deep Ordinal Regression NetworkCode1
M4Depth: Monocular depth estimation for autonomous vehicles in unseen environmentsCode1
Feature-metric Loss for Self-supervised Learning of Depth and EgomotionCode1
Digging Into Self-Supervised Monocular Depth EstimationCode1
Depth Estimation from Monocular Images and Sparse Radar DataCode1
Digging Into Uncertainty-based Pseudo-label for Robust Stereo MatchingCode1
Depth Estimation From Indoor Panoramas With Neural Scene RepresentationCode1
FDCT: Fast Depth Completion for Transparent ObjectsCode1
CL-MVSNet: Unsupervised Multi-View Stereo with Dual-Level Contrastive LearningCode1
Efficient Attention: Attention with Linear ComplexitiesCode1
MIMIC: Masked Image Modeling with Image CorrespondencesCode1
Mind The Edge: Refining Depth Edges in Sparsely-Supervised Monocular Depth EstimationCode1
A Practical Stereo Depth System for Smart GlassesCode1
MobileStereoNet: Towards Lightweight Deep Networks for Stereo MatchingCode1
Exploring Sparse Visual Prompt for Domain Adaptive Dense PredictionCode1
Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton RefinementCode1
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
ARAI-MVSNet: A multi-view stereo depth estimation network with adaptive depth range and depth intervalCode1
Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular DepthCode1
Depth estimation from 4D light field videosCode1
Monocular Visual-Inertial Depth EstimationCode1
Adaptive Surface Normal Constraint for Depth EstimationCode1
Disparity Estimation Using a Quad-Pixel SensorCode1
Automated Distance Estimation for Wildlife Camera TrappingCode1
DELTAS: Depth Estimation by Learning Triangulation And densification of Sparse pointsCode1
Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuningCode1
Collaboration Helps Camera Overtake LiDAR in 3D DetectionCode1
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary CellsCode1
Dusk Till Dawn: Self-supervised Nighttime Stereo Depth Estimation using Visual Foundation ModelsCode1
Are We Ready for Vision-Centric Driving Streaming Perception? The ASAP BenchmarkCode1
MTStereo 2.0: improved accuracy of stereo depth estimation withMax-treesCode1
Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth PredictionCode1
MulT: An End-to-End Multitask Learning TransformerCode1
Fine-grained Semantics-aware Representation Enhancement for Self-supervised Monocular Depth EstimationCode1
DiverseDepth: Affine-invariant Depth Prediction Using Diverse DataCode1
Depth Estimation by Combining Binocular Stereo and Monocular Structured-LightCode1
Event-based Stereo Depth Estimation from Ego-motion using Ray Density FusionCode1
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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