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 1151–1200 of 2454 papers

TitleStatusHype
A Systematic Literature Review on Deep Learning-based Depth Estimation in Computer Vision—0
Consistent Depth Prediction under Various Illuminations using Dilated Cross Attention—0
Du²Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels—0
Advancing Depth Anything Model for Unsupervised Monocular Depth Estimation in Endoscopy—0
Du^2Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels—0
Consistent Depth Prediction for Transparent Object Reconstruction from RGB-D Camera—0
Enhanced Depth Estimation and 3D Geometry Reconstruction using Bayesian Helmholtz Stereopsis with Belief Propagation—0
Low Power Depth Estimation of Rigid Objects for Time-of-Flight Imaging—0
DSCnet: Replicating Lidar Point Clouds with Deep Sensor Cloning—0
Drone Stereo Vision for Radiata Pine Branch Detection and Distance Measurement: Utilizing Deep Learning and YOLO Integration—0
Consistent Depth of Moving Objects in Video—0
DRL-ISP: Multi-Objective Camera ISP with Deep Reinforcement Learning—0
5D Light Field Synthesis from a Monocular Video—0
Driving Scene Perception Network: Real-time Joint Detection, Depth Estimation and Semantic Segmentation—0
Connecting the Dots: Learning Representations for Active Monocular Depth Estimation—0
DreamCube: 3D Panorama Generation via Multi-plane Synchronization—0
A Survey on Deep Learning Techniques for Stereo-based Depth Estimation—0
Low Compute and Fully Parallel Computer Vision With HashMatch—0
Low-rank Adaptation-based All-Weather Removal for Autonomous Navigation—0
M^33D: Learning 3D priors using Multi-Modal Masked Autoencoders for 2D image and video understanding—0
DPS-Net: Deep Polarimetric Stereo Depth Estimation—0
DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion—0
ADU-Depth: Attention-based Distillation with Uncertainty Modeling for Depth Estimation—0
DPBridge: Latent Diffusion Bridge for Dense Prediction—0
Gaussian Mixture based Evidential Learning for Stereo Matching—0
Long Range Object-Level Monocular Depth Estimation for UAVs—0
DoubleTake: Geometry Guided Depth Estimation—0
DoubleStar: Long-Range Attack Towards Depth Estimation based Obstacle Avoidance in Autonomous Systems—0
Configurable Holography: Towards Display and Scene Adaptation—0
Double Refinement Network for Efficient Indoor Monocular Depth Estimation—0
Confidence Guided Stereo 3D Object Detection with Split Depth Estimation—0
Adjusting Bias in Long Range Stereo Matching: A semantics guided approach—0
Don't Forget The Past: Recurrent Depth Estimation from Monocular Video—0
Domain-Transferred Synthetic Data Generation for Improving Monocular Depth Estimation—0
Confidence-Aware RGB-D Face Recognition via Virtual Depth Synthesis—0
Domain Decluttering: Simplifying Images to Mitigate Synthetic-Real Domain Shift and Improve Depth Estimation—0
Computing Egomotion with Local Loop Closures for Egocentric Videos—0
Adjust Your Focus: Defocus Deblurring From Dual-Pixel Images Using Explicit Multi-Scale Cross-Correlation—0
Domain Adaptive Monocular Depth Estimation With Semantic Information—0
Composite Learning for Robust and Effective Dense Predictions—0
Composite Focus Measure for High Quality Depth Maps—0
Does depth estimation help object detection?—0
A Deeper Insight into the UnDEMoN: Unsupervised Deep Network for Depth and Ego-Motion Estimation—0
LOLNeRF: Learn from One Look—0
Look Deeper into Depth: Monocular Depth Estimation with Semantic Booster and Attention-Driven Loss—0
DOC-Depth: A novel approach for dense depth ground truth generation—0
DO3D: Self-supervised Learning of Decomposed Object-aware 3D Motion and Depth from Monocular Videos—0
D-NPC: Dynamic Neural Point Clouds for Non-Rigid View Synthesis from Monocular Video—0
Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation—0
Comparison of Depth Estimation Setups from Stereo Endoscopy and Optical Tracking for Point Measurements—0
Show:102550
← PrevPage 24 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