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 951–1000 of 2454 papers

TitleStatusHype
Meta-Optimization for Higher Model Generalizability in Single-Image Depth Prediction—0
Learning Monocular Depth in Dynamic Environment via Context-aware Temporal Attention—0
Virtual Occlusions Through Implicit DepthCode1
A Multi-modal Approach to Single-modal Visual Place Classification—0
FusionDepth: Complement Self-Supervised Monocular Depth Estimation with Cost Volume—0
Improving 2D face recognition via fine-level facial depth generation and RGB-D complementary feature learning—0
Edge-aware Consistent Stereo Video Depth Estimation—0
High-Resolution Synthetic RGB-D Datasets for Monocular Depth Estimation—0
AutoColor: Learned Light Power Control for Multi-Color HologramsCode0
ViP-NeRF: Visibility Prior for Sparse Input Neural Radiance FieldsCode1
Exploring the Mutual Influence between Self-Supervised Single-Frame and Multi-Frame Depth EstimationCode0
CompletionFormer: Depth Completion with Convolutions and Vision TransformersCode2
Depth-Relative Self Attention for Monocular Depth Estimation—0
RGB-D Indiscernible Object Counting in Underwater ScenesCode1
FSNet: Redesign Self-Supervised MonoDepth for Full-Scale Depth Prediction for Autonomous Driving—0
A geometry-aware deep network for depth estimation in monocular endoscopyCode1
DarSwin: Distortion Aware Radial Swin Transformer—0
CrossFusion: Interleaving Cross-modal Complementation for Noise-resistant 3D Object Detection—0
Pose Constraints for Consistent Self-supervised Monocular Depth and Ego-motionCode0
Learning to Fuse Monocular and Multi-view Cues for Multi-frame Depth Estimation in Dynamic ScenesCode1
360^ High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer—0
EGformer: Equirectangular Geometry-biased Transformer for 360 Depth Estimation—0
Temporally Consistent Online Depth Estimation Using Point-Based FusionCode1
DINOv2: Learning Robust Visual Features without SupervisionCode6
The Second Monocular Depth Estimation Challenge—0
Self-Supervised Learning based Depth Estimation from Monocular ImagesCode0
Event-based tracking of human hands—0
iDisc: Internal Discretization for Monocular Depth EstimationCode3
Improving Neural Radiance Fields with Depth-aware Optimization for Novel View SynthesisCode0
BEVStereo++: Accurate Depth Estimation in Multi-view 3D Object Detection via Dynamic Temporal Stereo—0
DualRefine: Self-Supervised Depth and Pose Estimation Through Iterative Epipolar Sampling and Refinement Toward EquilibriumCode1
EGA-Depth: Efficient Guided Attention for Self-Supervised Multi-Camera Depth Estimation—0
DeLiRa: Self-Supervised Depth, Light, and Radiance Fields—0
DEFLOW: Self-supervised 3D Motion Estimation of Debris FlowCode1
Joint 2D-3D Multi-Task Learning on Cityscapes-3D: 3D Detection, Segmentation, and Depth EstimationCode2
altiro3D: Scene representation from single image and novel view synthesisCode1
ENRICH: Multi-purposE dataset for beNchmaRking In Computer vision and pHotogrammetryCode1
Single Image Depth Prediction Made Better: A Multivariate Gaussian Take—0
Joint Depth Estimation and Mixture of Rain Removal From a Single ImageCode0
EA-LSS: Edge-aware Lift-splat-shot Framework for 3D BEV Object DetectionCode2
SemHint-MD: Learning from Noisy Semantic Labels for Self-Supervised Monocular Depth Estimation—0
DDP: Diffusion Model for Dense Visual PredictionCode2
TiDy-PSFs: Computational Imaging with Time-Averaged Dynamic Point-Spread-Functions—0
DORT: Modeling Dynamic Objects in Recurrent for Multi-Camera 3D Object Detection and TrackingCode1
An intelligent modular real-time vision-based system for environment perceptionCode1
4K-HAZE: A Dehazing Benchmark with 4K Resolution Hazy and Haze-Free ImagesCode1
On the Importance of Accurate Geometry Data for Dense 3D Vision TasksCode1
Multi-Frame Self-Supervised Depth Estimation with Multi-Scale Feature Fusion in Dynamic Scenes—0
Collaboration Helps Camera Overtake LiDAR in 3D DetectionCode1
SCADE: NeRFs from Space Carving with Ambiguity-Aware Depth Estimates—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