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 451–500 of 2454 papers

TitleStatusHype
Detecting Car Speed using Object Detection and Depth Estimation: A Deep Learning Framework—0
Focal Depth Estimation: A Calibration-Free, Subject- and Daytime Invariant Approach—0
BodySLAM: A Generalized Monocular Visual SLAM Framework for Surgical ApplicationsCode1
Gaussian Mixture based Evidential Learning for Stereo Matching—0
Lumina-mGPT: Illuminate Flexible Photorealistic Text-to-Image Generation with Multimodal Generative PretrainingCode7
MCPDepth: Omnidirectional Depth Estimation via Stereo Matching from Multi-Cylindrical Panoramas—0
Embodiment: Self-Supervised Depth Estimation Based on Camera Models—0
High-Precision Self-Supervised Monocular Depth Estimation with Rich-Resource Prior—0
MonoMM: A Multi-scale Mamba-Enhanced Network for Real-time Monocular 3D Object Detection—0
EMatch: A Unified Framework for Event-based Optical Flow and Stereo Matching—0
Analysis and Improvement of Rank-Ordered Mean Algorithm in Single-Photon LiDAR—0
BaseBoostDepth: Exploiting Larger Baselines For Self-supervised Monocular Depth EstimationCode1
Improving 2D Feature Representations by 3D-Aware Fine-Tuning—0
Revisit Self-supervised Depth Estimation with Local Structure-from-Motion—0
HybridDepth: Robust Metric Depth Fusion by Leveraging Depth from Focus and Single-Image PriorsCode2
UMono: Physical Model Informed Hybrid CNN-Transformer Framework for Underwater Monocular Depth Estimation—0
BetterDepth: Plug-and-Play Diffusion Refiner for Zero-Shot Monocular Depth Estimation—0
Enhanced Depth Estimation and 3D Geometry Reconstruction using Bayesian Helmholtz Stereopsis with Belief Propagation—0
Physical Adversarial Attack on Monocular Depth Estimation via Shape-Varying Patches—0
DarSwin-Unet: Distortion Aware Encoder-Decoder Architecture—0
Diffusion Models for Monocular Depth Estimation: Overcoming Challenging ConditionsCode2
SINDER: Repairing the Singular Defects of DINOv2Code1
ToDER: Towards Colonoscopy Depth Estimation and Reconstruction with Geometry Constraint Adaptation—0
Mono-ViFI: A Unified Learning Framework for Self-supervised Single- and Multi-frame Monocular Depth EstimationCode2
Many Perception Tasks are Highly Redundant Functions of their Input Data—0
GroundUp: Rapid Sketch-Based 3D City Massing—0
Benchmarking Robust Self-Supervised Learning Across Diverse Downstream TasksCode0
Temporally Consistent Stereo MatchingCode2
OPEN: Object-wise Position Embedding for Multi-view 3D Object DetectionCode2
IDOL: Unified Dual-Modal Latent Diffusion for Human-Centric Joint Video-Depth GenerationCode2
Towards Scale-Aware Full Surround Monodepth with Transformers—0
LabelDistill: Label-guided Cross-modal Knowledge Distillation for Camera-based 3D Object DetectionCode1
ProDepth: Boosting Self-Supervised Multi-Frame Monocular Depth with Probabilistic FusionCode1
ScaleDepth: Decomposing Metric Depth Estimation into Scale Prediction and Relative Depth EstimationCode0
SCIPaD: Incorporating Spatial Clues into Unsupervised Pose-Depth Joint LearningCode1
A Physical Model-Guided Framework for Underwater Image Enhancement and Depth EstimationCode0
Towards Cross-View-Consistent Self-Supervised Surround Depth EstimationCode0
Parametric Modeling and Estimation of Photon Registrations for 3D Imaging—0
Depth-Aware Endoscopic Video InpaintingCode1
Uni-DVPS: Unified Model for Depth-Aware Video Panoptic SegmentationCode1
Physics-Inspired Deep Learning and Transferable Models for Bridge Scour Prediction—0
CaFNet: A Confidence-Driven Framework for Radar Camera Depth EstimationCode1
Deep Learning-based Depth Estimation Methods from Monocular Image and Videos: A Comprehensive Survey—0
Dense Monocular Motion Segmentation Using Optical Flow and Pseudo Depth Map: A Zero-Shot Approach—0
360 in the Wild: Dataset for Depth Prediction and View Synthesis—0
DoubleTake: Geometry Guided Depth Estimation—0
Depth-Guided Semi-Supervised Instance Segmentation—0
PID4LaTe: a physics-informed deep learning model for lake multi-depth temperature predictionCode0
Uncertainty and Self-Supervision in Single-View Depth—0
Splatter a Video: Video Gaussian Representation for Versatile Processing—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