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 1951–2000 of 2454 papers

TitleStatusHype
GEOcc: Geometrically Enhanced 3D Occupancy Network with Implicit-Explicit Depth Fusion and Contextual Self-Supervision—0
GeoDepth: From Point-to-Depth to Plane-to-Depth Modeling for Self-Supervised Monocular Depth Estimation—0
GeoFill: Reference-Based Image Inpainting with Better Geometric Understanding—0
GeoMan: Temporally Consistent Human Geometry Estimation using Image-to-Video Diffusion—0
Geometric Constraints in Deep Learning Frameworks: A Survey—0
Geometric Structure Based and Regularized Depth Estimation From 360 Indoor Imagery—0
Geometric Unsupervised Domain Adaptation for Semantic Segmentation—0
Exploring Geometric Consistency for Monocular 3D Object Detection—0
Geometry-Aware Network for Domain Adaptive Semantic Segmentation—0
Geometry-Based Next Frame Prediction from Monocular Video—0
GeometryCrafter: Consistent Geometry Estimation for Open-world Videos with Diffusion Priors—0
GGS: Generalizable Gaussian Splatting for Lane Switching in Autonomous Driving—0
Ghost-Stereo: GhostNet-based Cost Volume Enhancement and Aggregation for Stereo Matching Networks—0
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP—0
GLID: Pre-training a Generalist Encoder-Decoder Vision Model—0
Global-Local Network for Learning Depth with Very Sparse Supervision—0
GlocalFuse-Depth: Fusing Transformers and CNNs for All-day Self-supervised Monocular Depth Estimation—0
GMM: Delving into Gradient Aware and Model Perceive Depth Mining for Monocular 3D Detection—0
GPR-based Subsurface Object Detection and Reconstruction Using Random Motion and DepthNet—0
GPS-Gaussian+: Generalizable Pixel-wise 3D Gaussian Splatting for Real-Time Human-Scene Rendering from Sparse Views—0
GraphBEV: Towards Robust BEV Feature Alignment for Multi-Modal 3D Object Detection—0
GRIN: Zero-Shot Metric Depth with Pixel-Level Diffusion—0
GroundUp: Rapid Sketch-Based 3D City Massing—0
GScream: Learning 3D Geometry and Feature Consistent Gaussian Splatting for Object Removal—0
GSDC Transformer: An Efficient and Effective Cue Fusion for Monocular Multi-Frame Depth Estimation—0
GSGTrack: Gaussian Splatting-Guided Object Pose Tracking from RGB Videos—0
Guidance system for Visually Impaired Persons using Deep Learning and Optical flow—0
Guiding Local Feature Matching with Surface Curvature—0
GVDepth: Zero-Shot Monocular Depth Estimation for Ground Vehicles based on Probabilistic Cue Fusion—0
HandsOff: Labeled Dataset Generation With No Additional Human Annotations—0
Hand tracking for clinical applications: validation of the Google MediaPipe Hand (GMH) and the depth-enhanced GMH-D frameworks—0
Harnessing Foundation Models for Robust and Generalizable 6-DOF Bronchoscopy Localization—0
HC-Search for Structured Prediction in Computer Vision—0
HDPV-SLAM: Hybrid Depth-augmented Panoramic Visual SLAM for Mobile Mapping System with Tilted LiDAR and Panoramic Visual Camera—0
HeadPosr: End-to-end Trainable Head Pose Estimation using Transformer Encoders—0
Heterogeneous Light Fields—0
Hidden in plain sight: VLMs overlook their visual representations—0
Hierarchical Normalization for Robust Monocular Depth Estimation—0
High-Accuracy Facial Depth Models derived from 3D Synthetic Data—0
High-Accuracy RGB-D Face Recognition via Segmentation-Aware Face Depth Estimation and Mask-Guided Attention Network—0
High-fidelity Endoscopic Image Synthesis by Utilizing Depth-guided Neural Surfaces—0
High-Precision Self-Supervised Monocular Depth Estimation with Rich-Resource Prior—0
High Quality Structure From Small Motion for Rolling Shutter Cameras—0
High-Resolution Depth Estimation for 360-degree Panoramas through Perspective and Panoramic Depth Images Registration—0
High-Resolution Synthetic RGB-D Datasets for Monocular Depth Estimation—0
Hi-Map: Hierarchical Factorized Radiance Field for High-Fidelity Monocular Dense Mapping—0
HiMODE: A Hybrid Monocular Omnidirectional Depth Estimation Model—0
HMOR: Hierarchical Multi-Person Ordinal Relations for Monocular Multi-Person 3D Pose Estimation—0
H-Net: Unsupervised Attention-based Stereo Depth Estimation Leveraging Epipolar Geometry—0
HoloDrive: Holistic 2D-3D Multi-Modal Street Scene Generation for Autonomous Driving—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