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 1801–1850 of 2454 papers

TitleStatusHype
Soft Labels for Ordinal Regression—0
Solve the Puzzle of Instance Segmentation in Videos: A Weakly Supervised Framework with Spatio-Temporal Collaboration—0
SOSD-Net: Joint Semantic Object Segmentation and Depth Estimation from Monocular images—0
SpaceBlender: Creating Context-Rich Collaborative Spaces Through Generative 3D Scene Blending—0
Space-time Neural Irradiance Fields for Free-Viewpoint Video—0
Sparse2Dense: From direct sparse odometry to dense 3D reconstruction—0
Sparse Depth Completion with Semantic Mesh Deformation Optimization—0
Sparse LiDAR and Stereo Fusion (SLS-Fusion) for Depth Estimationand 3D Object Detection—0
Spatial and Angular Resolution Enhancement of Light Fields Using Convolutional Neural Networks—0
Spatial Correspondence With Generative Adversarial Network: Learning Depth From Monocular Videos—0
Spatially Visual Perception for End-to-End Robotic Learning—0
Spatial RoboGrasp: Generalized Robotic Grasping Control Policy—0
Spatio-Focal Bidirectional Disparity Estimation From a Dual-Pixel Image—0
Spectral 3D Computer Vision -- A Review—0
Spectrum-inspired Low-light Image Translation for Saliency Detection—0
Speed estimation evaluation on the KITTI benchmark based on motion and monocular depth information—0
SphereDepth: Panorama Depth Estimation from Spherical Domain—0
SphereFusion: Efficient Panorama Depth Estimation via Gated Fusion—0
SphereUFormer: A U-Shaped Transformer for Spherical 360 Perception—0
Spike-NeRF: Neural Radiance Field Based On Spike Camera—0
SpikeStereoNet: A Brain-Inspired Framework for Stereo Depth Estimation from Spike Streams—0
Splatter a Video: Video Gaussian Representation for Versatile Processing—0
SPLODE: Semi-Probabilistic Point and Line Odometry with Depth Estimation from RGB-D Camera Motion—0
SRFNet: Monocular Depth Estimation with Fine-grained Structure via Spatial Reliability-oriented Fusion of Frames and Events—0
SSAP: A Shape-Sensitive Adversarial Patch for Comprehensive Disruption of Monocular Depth Estimation in Autonomous Navigation Applications—0
StableGS: A Floater-Free Framework for 3D Gaussian Splatting—0
StandardSim: A Synthetic Dataset For Retail Environments—0
STATIC : Surface Temporal Affine for TIme Consistency in Video Monocular Depth Estimation—0
STDepthFormer: Predicting Spatio-temporal Depth from Video with a Self-supervised Transformer Model—0
SteReFo: Efficient Image Refocusing with Stereo Vision—0
Stereo4D: Learning How Things Move in 3D from Internet Stereo Videos—0
Stereo CenterNet based 3D Object Detection for Autonomous Driving—0
Stereo Correspondence and Reconstruction of Endoscopic Data Challenge—0
Stereo Dense Scene Reconstruction and Accurate Localization for Learning-Based Navigation of Laparoscope in Minimally Invasive Surgery—0
StereoDiff: Stereo-Diffusion Synergy for Video Depth Estimation—0
StereoGen: High-quality Stereo Image Generation from a Single Image—0
Stereo Matching by Self-supervision of Multiscopic Vision—0
Stereo-Matching Knowledge Distilled Monocular Depth Estimation Filtered by Multiple Disparity Consistency—0
Stereo Matching With Color and Monochrome Cameras in Low-Light Conditions—0
Stereo Object Matching Network—0
SteROI-D: System Design and Mapping for Stereo Depth Inference on Regions of Interest—0
StructNeRF: Neural Radiance Fields for Indoor Scenes with Structural Hints—0
Structural inference affects depth perception in the context of potential occlusion—0
Structure-Attentioned Memory Network for Monocular Depth Estimation—0
Structure-Aware Parametric Representations for Time-Resolved Light Transport—0
Structure-Aware Radar-Camera Depth Estimation—0
Structure-Centric Robust Monocular Depth Estimation via Knowledge Distillation—0
Structured Coupled Generative Adversarial Networks for Unsupervised Monocular Depth Estimation—0
Structured Depth Prediction in Challenging Monocular Video Sequences—0
Structured Prediction using cGANs with Fusion Discriminator—0
Show:102550
← PrevPage 37 of 50Next →
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