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 2051–2100 of 2454 papers

TitleStatusHype
Flow-Motion and Depth Network for Monocular Stereo and BeyondCode0
3D Ken Burns Effect from a Single ImageCode0
Structure-Attentioned Memory Network for Monocular Depth Estimation—0
Unsupervised Domain Adaptation for Depth Prediction from ImagesCode0
Robust Full-FoV Depth Estimation in Tele-wide Camera System—0
Auxiliary Learning for Deep Multi-task Learning—0
Depth Map Estimation for Free-Viewpoint Television—0
Unsupervised Video Depth Estimation Based on Ego-motion and Disparity Consensus—0
Deep Coarse-to-fine Dense Light Field Reconstruction with Flexible Sampling and Geometry-aware FusionCode0
UASOL, a large-scale high-resolution outdoor stereo dataset—0
Improving Self-Supervised Single View Depth Estimation by Masking OcclusionCode0
Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular VideoCode1
A2J: Anchor-to-Joint Regression Network for 3D Articulated Pose Estimation from a Single Depth ImageCode1
Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry—0
Indoor Depth Completion with Boundary Consistency and Self-AttentionCode0
n-MeRCI: A new Metric to Evaluate the Correlation Between Predictive Uncertainty and True Error—0
In defense of OSVOS—0
Distill Knowledge from NRSfM for Weakly Supervised 3D Pose Learning—0
OmniMVS: End-to-End Learning for Omnidirectional Stereo MatchingCode0
Task-Assisted Domain Adaptation with Anchor Tasks—0
To complete or to estimate, that is the question: A Multi-Task Approach to Depth Completion and Monocular Depth Estimation—0
Structured Coupled Generative Adversarial Networks for Unsupervised Monocular Depth Estimation—0
Index NetworkCode0
Exploiting temporal consistency for real-time video depth estimationCode0
Enhancing self-supervised monocular depth estimation with traditional visual odometry—0
Semi-Supervised Adversarial Monocular Depth Estimation—0
Unsupervised Learning of Depth and Deep Representation for Visual Odometry from Monocular Videos in a Metric Space—0
Adversarial View-Consistent Learning for Monocular Depth Estimation—0
Physical Cue based Depth-Sensing by Color Coding with Deaberration Network—0
A Framework for Depth Estimation and Relative Localization of Ground Robots using Computer Vision—0
Rapid Light Field Depth Estimation with Semi-Global Matching—0
Enforcing geometric constraints of virtual normal for depth predictionCode2
MultiDepth: Single-Image Depth Estimation via Multi-Task Regression and ClassificationCode0
A Compact Light Field Camera for Real-Time Depth Estimation—0
From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth EstimationCode1
SDNet: Semantically Guided Depth Estimation Network—0
RRNet: Repetition-Reduction Network for Energy Efficient Decoder of Depth Estimation—0
Conf-Net: Toward High-Confidence Dense 3D Point-Cloud with Error-Map PredictionCode0
Learning Depth from Monocular Videos Using Synthetic Data: A Temporally-Consistent Domain Adaptation Approach—0
Speed estimation evaluation on the KITTI benchmark based on motion and monocular depth information—0
Structure-Aware Residual Pyramid Network for Monocular Depth EstimationCode0
Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset TransferCode3
Pano Popups: Indoor 3D Reconstruction with a Plane-Aware Network—0
SLAM Endoscopy enhanced by adversarial depth prediction—0
Hard Pixel Mining for Depth Privileged Semantic SegmentationCode0
Mapped ConvolutionsCode0
Pixel-Accurate Depth Evaluation in Realistic Driving ScenariosCode0
SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks—0
Learning to Reconstruct and Understand Indoor Scenes from Sparse Views—0
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous DrivingCode0
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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