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 2351–2400 of 2454 papers

TitleStatusHype
OrchardDepth: Precise Metric Depth Estimation of Orchard Scene from Monocular Camera Images—0
Outdoor Monocular Depth Estimation: A Research Review—0
Out-of-focus: Learning Depth from Image Bokeh for Robotic Perception—0
P2D: a self-supervised method for depth estimation from polarimetry—0
π^3: Scalable Permutation-Equivariant Visual Geometry Learning—0
PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing—0
PanDA: Towards Panoramic Depth Anything with Unlabeled Panoramas and Mobius Spatial Augmentation—0
PanelNet: Understanding 360 Indoor Environment via Panel Representation—0
PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth Estimation—0
PanoGRF: Generalizable Spherical Radiance Fields for Wide-baseline Panoramas—0
Pano Popups: Indoor 3D Reconstruction with a Plane-Aware Network—0
Panoptic-Depth Color Map for Combination of Depth and Image Segmentation—0
Panoptic-Depth Forecasting—0
Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representation—0
Panoramic Stereo Videos With a Single Camera—0
Parallax Motion Effect Generation Through Instance Segmentation And Depth Estimation—0
Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation—0
Parametric Modeling and Estimation of Photon Registrations for 3D Imaging—0
Partially Supervised Multi-Task Network for Single-View Dietary Assessment—0
Passive Snapshot Coded Aperture Dual-Pixel RGB-D Imaging—0
PatchMVSNet: Patch-wise Unsupervised Multi-View Stereo for Weakly-Textured Surface Reconstruction—0
PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation—0
Pattern-Structure Diffusion for Multi-Task Learning—0
PCDepth: Pattern-based Complementary Learning for Monocular Depth Estimation by Best of Both Worlds—0
PDDM: Pseudo Depth Diffusion Model for RGB-PD Semantic Segmentation Based in Complex Indoor Scenes—0
Peeking Behind Objects: Layered Depth Prediction from a Single Image—0
People as Scene Probes—0
Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey—0
Perception Tokens Enhance Visual Reasoning in Multimodal Language Models—0
Perceptual Inductive Bias Is What You Need Before Contrastive Learning—0
Photo-realistic Neural Domain Randomization—0
Physical Adversarial Attack on Monocular Depth Estimation via Shape-Varying Patches—0
Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook—0
Physical Cue based Depth-Sensing by Color Coding with Deaberration Network—0
PIV-Based 3D Fluid Flow Reconstruction Using Light Field Camera—0
Pix2Point: Learning Outdoor 3D Using Sparse Point Clouds and Optimal Transport—0
Pixel-Aligned Multi-View Generation with Depth Guided Decoder—0
Pixel-Pair Occlusion Relationship Map (P2ORM): Formulation, Inference & Application—0
Play and Learn: Using Video Games to Train Computer Vision Models—0
Playing for Depth—0
PLG-IN: Pluggable Geometric Consistency Loss with Wasserstein Distance in Monocular Depth Estimation—0
PLT-D3: A High-fidelity Dynamic Driving Simulation Dataset for Stereo Depth and Scene Flow—0
PMPNet: Pixel Movement Prediction Network for Monocular Depth Estimation in Dynamic Scenes—0
Polarimetric Imaging for Perception—0
PolyMaX: General Dense Prediction with Mask Transformer—0
PosePilot: Steering Camera Pose for Generative World Models with Self-supervised Depth—0
Positional Information is All You Need: A Novel Pipeline for Self-Supervised SVDE from Videos—0
Position Estimation of Camera Based on Unsupervised Learning—0
Pow3R: Empowering Unconstrained 3D Reconstruction with Camera and Scene Priors—0
PPEA-Depth: Progressive Parameter-Efficient Adaptation for Self-Supervised Monocular Depth Estimation—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