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 1351–1400 of 2454 papers

TitleStatusHype
Unsupervised Deep Persistent Monocular Visual Odometry and Depth Estimation in Extreme Environments—0
Unsupervised High-Resolution Depth Learning From Videos With Dual Networks—0
Unsupervised Learning-based Depth Estimation aided Visual SLAM Approach—0
Unsupervised Learning Based Focal Stack Camera Depth Estimation—0
Unsupervised Learning of Depth and Deep Representation for Visual Odometry from Monocular Videos in a Metric Space—0
Unsupervised Learning of Depth Estimation and Visual Odometry for Sparse Light Field Cameras—0
Unsupervised Learning of Geometry with Edge-aware Depth-Normal Consistency—0
Unsupervised Learning of Monocular Depth Estimation with Bundle Adjustment, Super-Resolution and Clip Loss—0
Unsupervised Light Field Depth Estimation via Multi-view Feature Matching with Occlusion Prediction—0
Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics—0
Unsupervised Monocular Depth Estimation Based on Hierarchical Feature-Guided Diffusion—0
Unsupervised Monocular Depth Prediction for Indoor Continuous Video Streams—0
Unsupervised Monocular Depth Reconstruction of Non-Rigid Scenes—0
Unsupervised monocular stereo matching—0
Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training—0
Unsupervised Simultaneous Depth-from-defocus and Depth-from-focus—0
Unsupervised Simultaneous Learning for Camera Re-Localization and Depth Estimation from Video—0
Unsupervised Video Depth Estimation Based on Ego-motion and Disparity Consensus—0
Unveiling the Depths: A Multi-Modal Fusion Framework for Challenging Scenarios—0
USAM-Net: A U-Net-based Network for Improved Stereo Correspondence and Scene Depth Estimation using Features from a Pre-trained Image Segmentation network—0
USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution—0
UVCPNet: A UAV-Vehicle Collaborative Perception Network for 3D Object Detection—0
UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes—0
Variational Monocular Depth Estimation for Reliability Prediction—0
Versatile Depth Estimator Based on Common Relative Depth Estimation and Camera-Specific Relative-to-Metric Depth Conversion—0
VHS: High-Resolution Iterative Stereo Matching with Visual Hull Priors—0
Video Depth without Video Models—0
VirtualCube: An Immersive 3D Video Communication System—0
Vision and Learning for Deliberative Monocular Cluttered Flight—0
Vision-Based Environmental Perception for Autonomous Driving—0
Vision-based Perception for Autonomous Vehicles in Obstacle Avoidance Scenarios—0
Vision-Language Embodiment for Monocular Depth Estimation—0
Vision Transformer based Random Walk for Group Re-Identification—0
VistaDepth: Frequency Modulation With Bias Reweighting For Enhanced Long-Range Depth Estimation—0
VisualEchoes: Spatial Image Representation Learning through Echolocation—0
Visual Localization Using Semantic Segmentation and Depth Prediction—0
Visual Relationship Prediction via Label Clustering and Incorporation of Depth Information—0
V-MIND: Building Versatile Monocular Indoor 3D Detector with Diverse 2D Annotations—0
VolumeFusion: Deep Depth Fusion for 3D Scene Reconstruction—0
Volumetric Propagation Network: Stereo-LiDAR Fusion for Long-Range Depth Estimation—0
Voyager: Long-Range and World-Consistent Video Diffusion for Explorable 3D Scene Generation—0
VPLNet: Deep Single View Normal Estimation With Vanishing Points and Lines—0
Vyākarana: A Colorless Green Benchmark for Syntactic Evaluation in Indic Languages—0
Waterdrop Stereo—0
WaterNeRF: Neural Radiance Fields for Underwater Scenes—0
Wavelet Feature Maps Compression for Low Bandwidth Convolutional Neural Networks—0
WaveShot: A Compact Portable Unmanned Surface Vessel for Dynamic Water Surface Videography and Media Production—0
Weakly Supervised 3D Multi-person Pose Estimation for Large-scale Scenes based on Monocular Camera and Single LiDAR—0
Weakly-Supervised Monocular Depth Estimationwith Resolution-Mismatched Data—0
Web Stereo Video Supervision for Depth Prediction from Dynamic Scenes—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