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 1751–1800 of 2454 papers

TitleStatusHype
Semantic-Guided Representation Enhancement for Self-supervised Monocular Trained Depth Estimation—0
Semantic Object Prediction and Spatial Sound Super-Resolution with Binaural Sounds—0
Semantics-Depth-Symbiosis: Deeply Coupled Semi-Supervised Learning of Semantics and Depth—0
Semantics-Driven Unsupervised Learning for Monocular Depth and Ego-Motion Estimation—0
SemHint-MD: Learning from Noisy Semantic Labels for Self-Supervised Monocular Depth Estimation—0
Semi-Supervised Adversarial Monocular Depth Estimation—0
Semi-Supervised Learning with Mutual Distillation for Monocular Depth Estimation—0
Semi-supervised Multi-task Learning for Semantics and Depth—0
Semi-Supervised Semantic Depth Estimation using Symbiotic Transformer and NearFarMix Augmentation—0
Semi-Supervised Stereo-Based 3D Object Detection via Cross-View Consensus—0
Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry—0
SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks—0
SGE: Structured Light System Based on Gray Code with an Event Camera—0
SGFormer: Spherical Geometry Transformer for 360 Depth Estimation—0
SG-MIM: Structured Knowledge Guided Efficient Pre-training for Dense Prediction—0
SGTBN: Generating Dense Depth Maps from Single-Line LiDAR—0
Shakes on a Plane: Unsupervised Depth Estimation from Unstabilized Photography—0
Shape Consistent 2D Keypoint Estimation under Domain Shift—0
SharpDepth: Sharpening Metric Depth Predictions Using Diffusion Distillation—0
Shift R-CNN: Deep Monocular 3D Object Detection with Closed-Form Geometric Constraints—0
SIDE: Center-based Stereo 3D Detector with Structure-aware Instance Depth Estimation—0
SideRT: A Real-time Pure Transformer Architecture for Single Image Depth Estimation—0
SimHaze: game engine simulated data for real-world dehazing—0
SimpleBEV: Improved LiDAR-Camera Fusion Architecture for 3D Object Detection—0
SimpleMapping: Real-Time Visual-Inertial Dense Mapping with Deep Multi-View Stereo—0
Simplify Implant Depth Prediction as Video Grounding: A Texture Perceive Implant Depth Prediction Network—0
SimuGAN: Unsupervised forward modeling and optimal design of a LIDAR Camera—0
Simultaneous Estimation of Near IR BRDF and Fine-Scale Surface Geometry—0
Simultaneous Localization And Mapping with depth Prediction using Capsule Networks for UAVs—0
Single Image Depth Estimation: An Overview—0
Single Image Depth Estimation Based on Spectral Consistency and Predicted View—0
Single Image Depth Prediction Made Better: A Multivariate Gaussian Take—0
Single-Shot Metric Depth from Focused Plenoptic Cameras—0
Single-Stage 3D Geometry-Preserving Depth Estimation Model Training on Dataset Mixtures with Uncalibrated Stereo Data—0
Single-Stage Is Enough: Multi-Person Absolute 3D Pose Estimation—0
Single-View and Multi-View Depth Fusion—0
Single View Depth Estimation from Examples—0
Size-to-depth: A New Perspective for Single Image Depth Estimation—0
Skin the sheep not only once: Reusing Various Depth Datasets to Drive the Learning of Optical Flow—0
Skip-and-Play: Depth-Driven Pose-Preserved Image Generation for Any Objects—0
SlaBins: Fisheye Depth Estimation using Slanted Bins on Road Environments—0
SLAM Endoscopy enhanced by adversarial depth prediction—0
SLAM in the Dark: Self-Supervised Learning of Pose, Depth and Loop-Closure from Thermal Images—0
SLAM in the Field: An Evaluation of Monocular Mapping and Localization on Challenging Dynamic Agricultural Environment—0
SliceNet: Deep Dense Depth Estimation From a Single Indoor Panorama Using a Slice-Based Representation—0
SM3D: Simultaneous Monocular Mapping and 3D Detection—0
SMUDLP: Self-Teaching Multi-Frame Unsupervised Endoscopic Depth Estimation with Learnable Patchmatch—0
Snapshot Compressed Imaging Based Single-Measurement Computer Vision for Videos—0
Soccer on Your Tabletop—0
SoftEnNet: Symbiotic Monocular Depth Estimation and Lumen Segmentation for Colonoscopy Endorobots—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