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 1201–1250 of 2454 papers

TitleStatusHype
Dark Channel-Assisted Depth-from-Defocus from a Single Image—0
DarSwin: Distortion Aware Radial Swin Transformer—0
DarSwin-Unet: Distortion Aware Encoder-Decoder Architecture—0
Data-Driven Method for Enhanced Corrosion Assessment of Reinforced Concrete Structures—0
Dense Depth Distillation with Out-of-Distribution Simulated Images—0
DATAP-SfM: Dynamic-Aware Tracking Any Point for Robust Structure from Motion in the Wild—0
DB3D-L: Depth-aware BEV Feature Transformation for Accurate 3D Lane Detection—0
DCIRNet: Depth Completion with Iterative Refinement for Dexterous Grasping of Transparent and Reflective Objects—0
DCPI-Depth: Explicitly Infusing Dense Correspondence Prior to Unsupervised Monocular Depth Estimation—0
DDOS: The Drone Depth and Obstacle Segmentation Dataset—0
DD-VNB: A Depth-based Dual-Loop Framework for Real-time Visually Navigated Bronchoscopy—0
Dead Time Compensation for High-Flux Ranging—0
Decoder Modulation for Indoor Depth Completion—0
Decomposition-based and Interference Perception for Infrared and Visible Image Fusion in Complex Scenes—0
Deep 3D Pan via adaptive "t-shaped" convolutions with global and local adaptive dilations—0
Deep 3D Pan via Local adaptive "t-shaped" convolutions with global and local adaptive dilations—0
Deep Anti-aliasing of Whole Focal Stack Using Slice Spectrum—0
Deep Classification Network for Monocular Depth Estimation—0
Deep Convolutional Neural Fields for Depth Estimation from a Single Image—0
Deep cross-domain building extraction for selective depth estimation from oblique aerial imagery—0
Deep Depth From Aberration Map—0
Deep Depth from Focal Stack with Defocus Model for Camera-Setting Invariance—0
Deep Event Stereo Leveraged by Event-to-Image Translation—0
Deep feature fusion for self-supervised monocular depth prediction—0
DeepFusion: A Robust and Modular 3D Object Detector for Lidars, Cameras and Radars—0
Deep Imbalanced Regression via Hierarchical Classification Adjustment—0
Deep Learning and Conditional Random Fields-based Depth Estimation and Topographical Reconstruction from Conventional Endoscopy—0
Deep Learning-based Depth Estimation Methods from Monocular Image and Videos: A Comprehensive Survey—0
Deep Learning based Monocular Depth Prediction: Datasets, Methods and Applications—0
Deep-Learning-Based Single-Image Height Reconstruction from Very-High-Resolution SAR Intensity Data—0
Deep Learning with Cinematic Rendering: Fine-Tuning Deep Neural Networks Using Photorealistic Medical Images—0
DeepLens: Shallow Depth Of Field From A Single Image—0
DeepMetricEye: Metric Depth Estimation in Periocular VR Imagery—0
Deep Monocular 3D Human Pose Estimation via Cascaded Dimension-Lifting—0
Deep multi-scale architectures for monocular depth estimation—0
Deep Neural Networks for Accurate Depth Estimation with Latent Space Features—0
Constructive Universal Approximation and Finite Sample Memorization by Narrow Deep ReLU Networks—0
Deep Optics for Monocular Depth Estimation and 3D Object Detection—0
Deep Phase Coded Image Prior—0
Deep Planar Parallax for Monocular Depth Estimation—0
DeepRelativeFusion: Dense Monocular SLAM using Single-Image Relative Depth Prediction—0
Deep Robotic Prediction with hierarchical RGB-D Fusion—0
Deep Robust Single Image Depth Estimation Neural Network Using Scene Understanding—0
Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry—0
DeepWheel: Generating a 3D Synthetic Wheel Dataset for Design and Performance Evaluation—0
Deflating Dataset Bias Using Synthetic Data Augmentation—0
Defogging Kinect: Simultaneous Estimation of Object Region and Depth in Foggy Scenes—0
DEHRFormer: Real-time Transformer for Depth Estimation and Haze Removal from Varicolored Haze Scenes—0
DejaVu: Conditional Regenerative Learning to Enhance Dense Prediction—0
DeLiRa: Self-Supervised Depth, Light, and Radiance Fields—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