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 801–850 of 2454 papers

TitleStatusHype
Omnidirectional Depth-Aided Occupancy Prediction based on Cylindrical Voxel for Autonomous Driving—0
Tracktention: Leveraging Point Tracking to Attend Videos Faster and Better—0
FUSE: Label-Free Image-Event Joint Monocular Depth Estimation via Frequency-Decoupled Alignment and Degradation-Robust FusionCode0
Semi-SD: Semi-Supervised Metric Depth Estimation via Surrounding Cameras for Autonomous DrivingCode0
StableGS: A Floater-Free Framework for 3D Gaussian Splatting—0
PDDM: Pseudo Depth Diffusion Model for RGB-PD Semantic Segmentation Based in Complex Indoor Scenes—0
Co-SemDepth: Fast Joint Semantic Segmentation and Depth Estimation on Aerial ImagesCode0
Pow3R: Empowering Unconstrained 3D Reconstruction with Camera and Scene Priors—0
GAA-TSO: Geometry-Aware Assisted Depth Completion for Transparent and Specular Objects—0
Radar-Guided Polynomial Fitting for Metric Depth Estimation—0
Dynamic Point Maps: A Versatile Representation for Dynamic 3D Reconstruction—0
Jasmine: Harnessing Diffusion Prior for Self-supervised Depth Estimation—0
Learning to Efficiently Adapt Foundation Models for Self-Supervised Endoscopic 3D Scene Reconstruction from Any Cameras—0
TULIP: Towards Unified Language-Image Pretraining—0
EgoDTM: Towards 3D-Aware Egocentric Video-Language PretrainingCode0
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
Multi-view Reconstruction via SfM-guided Monocular Depth Estimation—0
3D Densification for Multi-Map Monocular VSLAM in Endoscopy—0
DUNE: Distilling a Universal Encoder from Heterogeneous 2D and 3D Teachers—0
MonoCT: Overcoming Monocular 3D Detection Domain Shift with Consistent Teacher Models—0
Improving Geometric Consistency for 360-Degree Neural Radiance Fields in Indoor Scenarios—0
Seeing and Seeing Through the Glass: Real and Synthetic Data for Multi-Layer Depth Estimation—0
Flow-NeRF: Joint Learning of Geometry, Poses, and Dense Flow within Unified Neural Representations—0
WonderVerse: Extendable 3D Scene Generation with Video Generative Models—0
GarmentCrafter: Progressive Novel View Synthesis for Single-View 3D Garment Reconstruction and Editing—0
Endo-FASt3r: Endoscopic Foundation model Adaptation for Structure from motion—0
Towards Ambiguity-Free Spatial Foundation Model: Rethinking and Decoupling Depth AmbiguityCode0
TomatoScanner: phenotyping tomato fruit based on only RGB imageCode0
EvidMTL: Evidential Multi-Task Learning for Uncertainty-Aware Semantic Surface Mapping from Monocular RGB Images—0
Multi-View Depth Consistent Image Generation Using Generative AI Models: Application on Architectural Design of University Buildings—0
RGB-Thermal Infrared Fusion for Robust Depth Estimation in Complex Environments—0
Task-Agnostic Attacks Against Vision Foundation ModelsCode0
SLAM in the Dark: Self-Supervised Learning of Pose, Depth and Loop-Closure from Thermal Images—0
RGB-Only Gaussian Splatting SLAM for Unbounded Outdoor Scenes—0
Self-supervised Monocular Depth Estimation Robust to Reflective Surface Leveraged by Triplet Mining—0
OrchardDepth: Precise Metric Depth Estimation of Orchard Scene from Monocular Camera Images—0
Challenges of Multi-Modal Coreset Selection for Depth PredictionCode0
LXLv2: Enhanced LiDAR Excluded Lean 3D Object Detection with Fusion of 4D Radar and Camera—0
SHADeS: Self-supervised Monocular Depth Estimation Through Non-Lambertian Image DecompositionCode0
Pre-training Auto-regressive Robotic Models with 4D Representations—0
Deep Neural Networks for Accurate Depth Estimation with Latent Space Features—0
Adjust Your Focus: Defocus Deblurring From Dual-Pixel Images Using Explicit Multi-Scale Cross-Correlation—0
RealCam-I2V: Real-World Image-to-Video Generation with Interactive Complex Camera Control—0
CoL3D: Collaborative Learning of Single-view Depth and Camera Intrinsics for Metric 3D Shape Recovery—0
SteROI-D: System Design and Mapping for Stereo Depth Inference on Regions of Interest—0
S^2-Diffusion: Generalizing from Instance-level to Category-level Skills in Robot Manipulation—0
Matrix3D: Large Photogrammetry Model All-in-One—0
Learning Inverse Laplacian Pyramid for Progressive Depth Completion—0
From Image to Video: An Empirical Study of Diffusion Representations—0
Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing—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