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 351–400 of 2454 papers

TitleStatusHype
UnCLe: Unsupervised Continual Learning of Depth Completion—0
YOLO11 and Vision Transformers based 3D Pose Estimation of Immature Green Fruits in Commercial Apple Orchards for Robotic Thinning—0
TIPS: Text-Image Pretraining with Spatial AwarenessCode2
DCDepth: Progressive Monocular Depth Estimation in Discrete Cosine DomainCode1
DepthSplat: Connecting Gaussian Splatting and DepthCode5
DH-VTON: Deep Text-Driven Virtual Try-On via Hybrid Attention Learning—0
CVCP-Fusion: On Implicit Depth Estimation for 3D Bounding Box PredictionCode0
Enhanced Encoder-Decoder Architecture for Accurate Monocular Depth EstimationCode0
Few-shot Novel View Synthesis using Depth Aware 3D Gaussian SplattingCode2
Improved Depth Estimation of Bayesian Neural NetworksCode0
Depth Any Video with Scalable Synthetic DataCode4
When Does Perceptual Alignment Benefit Vision Representations?Code4
Color-Guided Flying Pixel Correction in Depth ImagesCode0
Surgical Depth Anything: Depth Estimation for Surgical Scenes using Foundation Models—0
Analysis of different disparity estimation techniques on aerial stereo image datasets—0
Structure-Centric Robust Monocular Depth Estimation via Knowledge Distillation—0
CUBE360: Learning Cubic Field Representation for Monocular 360 Depth Estimation for Virtual Reality—0
Vision Transformer based Random Walk for Group Re-Identification—0
EndoPerfect: High-Accuracy Monocular Depth Estimation and 3D Reconstruction for Endoscopic Surgery via NeRF-Stereo Fusion—0
Refinement of Monocular Depth Maps via Multi-View Differentiable RenderingCode2
RSA: Resolving Scale Ambiguities in Monocular Depth Estimators through Language DescriptionsCode0
Depth Pro: Sharp Monocular Metric Depth in Less Than a SecondCode9
Drone Stereo Vision for Radiata Pine Branch Detection and Distance Measurement: Utilizing Deep Learning and YOLO Integration—0
Seamless Augmented Reality Integration in Arthroscopy: A Pipeline for Articular Reconstruction and Guidance—0
Towards Full-parameter and Parameter-efficient Self-learning For Endoscopic Camera Depth Estimation—0
EndoDepth: A Benchmark for Assessing Robustness in Endoscopic Depth PredictionCode1
CCDepth: A Lightweight Self-supervised Depth Estimation Network with Enhanced Interpretability—0
fCOP: Focal Length Estimation from Category-level Object Priors—0
KineDepth: Utilizing Robot Kinematics for Online Metric Depth Estimation—0
Event-based Stereo Depth Estimation: A SurveyCode2
Self-Distilled Depth Refinement with Noisy Poisson FusionCode1
Self-supervised Monocular Depth Estimation with Large Kernel Attention—0
ViewpointDepth: A New Dataset for Monocular Depth Estimation Under Viewpoint Shifts—0
Optical Lens Attack on Deep Learning Based Monocular Depth Estimation—0
3DDX: Bone Surface Reconstruction from a Single Standard-Geometry Radiograph via Dual-Face Depth EstimationCode0
Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation—0
EventHDR: from Event to High-Speed HDR Videos and Beyond—0
Benchmarking Robustness of Endoscopic Depth Estimation with Synthetically Corrupted DataCode0
DepthART: Monocular Depth Estimation as Autoregressive Refinement Task—0
FisheyeDepth: A Real Scale Self-Supervised Depth Estimation Model for Fisheye CameraCode1
Robust and Flexible Omnidirectional Depth Estimation with Multiple 360° Cameras—0
Generalizing monocular colonoscopy image depth estimation by uncertainty-based global and local fusion network—0
UniBEVFusion: Unified Radar-Vision BEVFusion for 3D Object Detection—0
GroCo: Ground Constraint for Metric Self-Supervised Monocular DepthCode1
@Bench: Benchmarking Vision-Language Models for Human-centered Assistive Technology—0
DAP-LED: Learning Degradation-Aware Priors with CLIP for Joint Low-light Enhancement and Deblurring—0
CVT-Occ: Cost Volume Temporal Fusion for 3D Occupancy PredictionCode2
SpaceBlender: Creating Context-Rich Collaborative Spaces Through Generative 3D Scene Blending—0
Interpretable Action Recognition on Hard to Classify Actions—0
Depth Estimation Based on 3D Gaussian Splatting Siamese Defocus—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