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

Showing 20762100 of 2454 papers

TitleStatusHype
Task-Aware Monocular Depth Estimation for 3D Object DetectionCode0
Temporally Consistent Depth Prediction with Flow-Guided Memory Units0
Deep Robotic Prediction with hierarchical RGB-D Fusion0
3D Ken Burns Effect from a Single ImageCode0
Flow-Motion and Depth Network for Monocular Stereo and BeyondCode0
Structure-Attentioned Memory Network for Monocular Depth Estimation0
Unsupervised Domain Adaptation for Depth Prediction from ImagesCode0
Robust Full-FoV Depth Estimation in Tele-wide Camera System0
Auxiliary Learning for Deep Multi-task Learning0
Depth Map Estimation for Free-Viewpoint Television0
Unsupervised Video Depth Estimation Based on Ego-motion and Disparity Consensus0
Deep Coarse-to-fine Dense Light Field Reconstruction with Flexible Sampling and Geometry-aware FusionCode0
UASOL, a large-scale high-resolution outdoor stereo dataset0
Improving Self-Supervised Single View Depth Estimation by Masking OcclusionCode0
Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry0
Indoor Depth Completion with Boundary Consistency and Self-AttentionCode0
n-MeRCI: A new Metric to Evaluate the Correlation Between Predictive Uncertainty and True Error0
In defense of OSVOS0
Distill Knowledge from NRSfM for Weakly Supervised 3D Pose Learning0
OmniMVS: End-to-End Learning for Omnidirectional Stereo MatchingCode0
Task-Assisted Domain Adaptation with Anchor Tasks0
Structured Coupled Generative Adversarial Networks for Unsupervised Monocular Depth Estimation0
To complete or to estimate, that is the question: A Multi-Task Approach to Depth Completion and Monocular Depth Estimation0
Index NetworkCode0
Exploiting temporal consistency for real-time video depth estimationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1OmniDepthRMSE0.62Unverified
2SphereDepthRMSE0.45Unverified
3Jin et al.RMSE0.42Unverified
4BiFuse with fusionRMSE0.41Unverified
5HoHoNet (ResNet-101)RMSE0.38Unverified
6PanoDepthRMSE0.37Unverified
7BiFuse++RMSE0.37Unverified
8UniFuse with fusionRMSE0.37Unverified
9DisConvRMSE0.37Unverified
10SliceNetRMSE0.37Unverified
#ModelMetricClaimedVerifiedStatus
1A2JmAP8.61Unverified
2PAD-NetRMS0.79Unverified
3MS-CRFRMS0.59Unverified
4DORNRMS0.51Unverified
5FreeformRMS0.43Unverified
6Optimized, freeformRMS0.43Unverified
7VNLRMS0.42Unverified
8BTSRMS0.41Unverified
9TransDepth (AGD+ ViT)RMS0.37Unverified
10AdaBinsRMS0.36Unverified
#ModelMetricClaimedVerifiedStatus
1T2NetAbs Rel0.35Unverified
2MIDASAbs Rel0.31Unverified
3Bhattacharjee et al.Abs Rel0.25Unverified
#ModelMetricClaimedVerifiedStatus
1T2NetAbs Rel0.49Unverified
2MIDASAbs Rel0.42Unverified
3Bhattacharjee et al.Abs Rel0.38Unverified
#ModelMetricClaimedVerifiedStatus
1LeReSabsolute relative error0.1Unverified
2DELTASabsolute relative error0.09Unverified
3Distill Any Depthabsolute relative error0.04Unverified
#ModelMetricClaimedVerifiedStatus
1SDC-DepthRMSE6.92Unverified
2SwinMTLRMSE6.35Unverified
#ModelMetricClaimedVerifiedStatus
1AIP-BrownDelta < 1.250.36Unverified
2LeResDelta < 1.250.23Unverified
#ModelMetricClaimedVerifiedStatus
1H-Net (Ours)Absolute relative error (AbsRel)0.09Unverified
2H-Net (Ours) Full EigenAbsolute relative error (AbsRel)0.08Unverified
#ModelMetricClaimedVerifiedStatus
1GLPDepthDelta < 1.250.43Unverified
2SRDINET (Model A)Delta < 1.250.4Unverified
#ModelMetricClaimedVerifiedStatus
1Atlas (finetuned)RMSE0.17Unverified
2Atlas (plain)RMSE0.17Unverified
#ModelMetricClaimedVerifiedStatus
1LFattNetBadPix(0.01)17.23Unverified
#ModelMetricClaimedVerifiedStatus
1LightDepthNumber of parameters (M)42.6Unverified
#ModelMetricClaimedVerifiedStatus
1UniFuseAbs Rel0.11Unverified
#ModelMetricClaimedVerifiedStatus
1X-TC (Cross-Task Consistency)L1 error1.63Unverified