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 2001–2050 of 2454 papers

TitleStatusHype
Voyager: Long-Range and World-Consistent Video Diffusion for Explorable 3D Scene Generation—0
VPLNet: Deep Single View Normal Estimation With Vanishing Points and Lines—0
Vyākarana: A Colorless Green Benchmark for Syntactic Evaluation in Indic Languages—0
Waterdrop Stereo—0
WaterNeRF: Neural Radiance Fields for Underwater Scenes—0
Wavelet Feature Maps Compression for Low Bandwidth Convolutional Neural Networks—0
WaveShot: A Compact Portable Unmanned Surface Vessel for Dynamic Water Surface Videography and Media Production—0
Weakly Supervised 3D Multi-person Pose Estimation for Large-scale Scenes based on Monocular Camera and Single LiDAR—0
Weakly-Supervised Monocular Depth Estimationwith Resolution-Mismatched Data—0
Web Stereo Video Supervision for Depth Prediction from Dynamic Scenes—0
What Sparse Light Field Coding Reveals About Scene Structure—0
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey—0
WildFusion: Learning 3D-Aware Latent Diffusion Models in View Space—0
Wild ToFu: Improving Range and Quality of Indirect Time-of-Flight Depth with RGB Fusion in Challenging Environments—0
Win-Win: Training High-Resolution Vision Transformers from Two Windows—0
Wireless Software Synchronization of Multiple Distributed Cameras—0
WonderVerse: Extendable 3D Scene Generation with Video Generative Models—0
WonderWorld: Interactive 3D Scene Generation from a Single Image—0
WS-SfMLearner: Self-supervised Monocular Depth and Ego-motion Estimation on Surgical Videos with Unknown Camera Parameters—0
X-Distill: Improving Self-Supervised Monocular Depth via Cross-Task Distillation—0
YOLO11 and Vision Transformers based 3D Pose Estimation of Immature Green Fruits in Commercial Apple Orchards for Robotic Thinning—0
Zero-BEV: Zero-shot Projection of Any First-Person Modality to BEV Maps—0
Zero-Shot Metric Depth with a Field-of-View Conditioned Diffusion Model—0
Zero-Shot Monocular Scene Flow Estimation in the Wild—0
Zero-Shot Novel View and Depth Synthesis with Multi-View Geometric Diffusion—0
Pre-training Auto-regressive Robotic Models with 4D Representations—0
PriorDiffusion: Leverage Language Prior in Diffusion Models for Monocular Depth Estimation—0
Privacy-Preserving Operating Room Workflow Analysis using Digital Twins—0
Probabilistic and Geometric Depth: Detecting Objects in Perspective—0
Probabilistic Graph Attention Network with Conditional Kernels for Pixel-Wise Prediction—0
Progressive Depth Learning for Single Image Dehazing—0
Promoting CNNs with Cross-Architecture Knowledge Distillation for Efficient Monocular Depth Estimation—0
PromptDet: A Lightweight 3D Object Detection Framework with LiDAR Prompts—0
PromptMono: Cross Prompting Attention for Self-Supervised Monocular Depth Estimation in Challenging Environments—0
PS^2F: Polarized Spiral Point Spread Function for Single-Shot 3D Sensing—0
Pseudo Label-Guided Multi Task Learning for Scene Understanding—0
Pseudo-LiDAR Based Road Detection—0
Pseudo Supervised Monocular Depth Estimation with Teacher-Student Network—0
Pulling Things out of Perspective—0
Pyramid Feature Attention Network for Monocular Depth Prediction—0
Pyramid Frequency Network with Spatial Attention Residual Refinement Module for Monocular Depth Estimation—0
Q-SLAM: Quadric Representations for Monocular SLAM—0
R3D3: Dense 3D Reconstruction of Dynamic Scenes from Multiple Cameras—0
R^3SGM: Real-time Raster-Respecting Semi-Global Matching for Power-Constrained Systems—0
R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes—0
RaCalNet: Radar Calibration Network for Sparse-Supervised Metric Depth Estimation—0
Radar-Guided Polynomial Fitting for Metric Depth Estimation—0
Rain rendering for evaluating and improving robustness to bad weather—0
Rapid Flood Inundation Forecast Using Fourier Neural Operator—0
Rapid Light Field Depth Estimation with Semi-Global Matching—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