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 801–850 of 2454 papers

TitleStatusHype
A Large RGB-D Dataset for Semi-supervised Monocular Depth Estimation—0
Improving Depth Estimation using Location Information—0
GMM: Delving into Gradient Aware and Model Perceive Depth Mining for Monocular 3D Detection—0
Deep Learning based Monocular Depth Prediction: Datasets, Methods and Applications—0
Deep Learning-based Depth Estimation Methods from Monocular Image and Videos: A Comprehensive Survey—0
Deep Learning and Conditional Random Fields-based Depth Estimation and Topographical Reconstruction from Conventional Endoscopy—0
Deep Imbalanced Regression via Hierarchical Classification Adjustment—0
3D endoscopic depth estimation using 3D surface-aware constraints—0
GPR-based Subsurface Object Detection and Reconstruction Using Random Motion and DepthNet—0
DeepFusion: A Robust and Modular 3D Object Detector for Lidars, Cameras and Radars—0
Deep feature fusion for self-supervised monocular depth prediction—0
Deep Event Stereo Leveraged by Event-to-Image Translation—0
@Bench: Benchmarking Vision-Language Models for Human-centered Assistive Technology—0
A Hybrid mmWave and Camera System for Long-Range Depth Imaging—0
Flexible Depth Completion for Sparse and Varying Point Densities—0
Bayesian Depth-from-Defocus with Shading Constraints—0
AcED: Accurate and Edge-consistent Monocular Depth Estimation—0
GlocalFuse-Depth: Fusing Transformers and CNNs for All-day Self-supervised Monocular Depth Estimation—0
Deep Depth From Aberration Map—0
FLaME: Fast Lightweight Mesh Estimation Using Variational Smoothing on Delaunay Graphs—0
Bayesian Deep Basis Fitting for Depth Completion with Uncertainty—0
FisheyeDistill: Self-Supervised Monocular Depth Estimation with Ordinal Distillation for Fisheye Cameras—0
FIReStereo: Forest InfraRed Stereo Dataset for UAS Depth Perception in Visually Degraded Environments—0
A High Resolution Multi-exposure Stereoscopic Image & Video Database of Natural Scenes—0
Deep cross-domain building extraction for selective depth estimation from oblique aerial imagery—0
Fine Dense Alignment of Image Bursts through Camera Pose and Depth Estimation—0
A high-precision self-supervised monocular visual odometry in foggy weather based on robust cycled generative adversarial networks and multi-task learning aided depth estimation—0
FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation—0
FG-Depth: Flow-Guided Unsupervised Monocular Depth Estimation—0
Deep Convolutional Neural Fields for Depth Estimation from a Single Image—0
FewViewGS: Gaussian Splatting with Few View Matching and Multi-stage Training—0
Balancing Shared and Task-Specific Representations: A Hybrid Approach to Depth-Aware Video Panoptic Segmentation—0
SurgPose: Generalisable Surgical Instrument Pose Estimation using Zero-Shot Learning and Stereo Vision—0
Global-Local Network for Learning Depth with Very Sparse Supervision—0
GraphBEV: Towards Robust BEV Feature Alignment for Multi-Modal 3D Object Detection—0
FEDORA: Flying Event Dataset fOr Reactive behAvior—0
Federated Self-Supervised Learning of Monocular Depth Estimators for Autonomous Vehicles—0
Feature-Level Collaboration: Joint Unsupervised Learning of Optical Flow, Stereo Depth and Camera Motion—0
Deep Classification Network for Monocular Depth Estimation—0
Deep Anti-aliasing of Whole Focal Stack Using Slice Spectrum—0
fCOP: Focal Length Estimation from Category-level Object Priors—0
FIS-Nets: Full-image Supervised Networks for Monocular Depth Estimation—0
f-Cal: Calibrated aleatoric uncertainty estimation from neural networks for robot perception—0
Balanced Depth Completion between Dense Depth Inference and Sparse Range Measurements via KISS-GP—0
3D Distillation: Improving Self-Supervised Monocular Depth Estimation on Reflective Surfaces—0
Fast Underwater Scene Reconstruction using Multi-View Stereo and Physical Imaging—0
Deep 3D Pan via Local adaptive "t-shaped" convolutions with global and local adaptive dilations—0
Deep 3D Pan via adaptive "t-shaped" convolutions with global and local adaptive dilations—0
Deep Depth from Focal Stack with Defocus Model for Camera-Setting Invariance—0
BadDepth: Backdoor Attacks Against Monocular Depth Estimation in the Physical World—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