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 1401–1450 of 2454 papers

TitleStatusHype
NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth EstimationCode2
Occlusion-Aware Cost Constructor for Light Field Depth EstimationCode1
OmniFusion: 360 Monocular Depth Estimation via Geometry-Aware FusionCode1
Instance-aware multi-object self-supervision for monocular depth prediction—0
MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and tasksCode1
Detecting Adversarial Perturbations in Multi-Task PerceptionCode0
How Much Depth Information can Radar Contribute to a Depth Estimation Model?—0
Deep Depth from Focal Stack with Defocus Model for Camera-Setting Invariance—0
On Monocular Depth Estimation and Uncertainty Quantification using Classification Approaches for Regression—0
Light Robust Monocular Depth Estimation For Outdoor Environment Via Monochrome And Color Camera Fusion—0
N-QGN: Navigation Map from a Monocular Camera using Quadtree Generating Networks—0
Synthesizing Photorealistic Images with Deep Generative Learning—0
Joint Learning of Frequency and Spatial Domains for Dense Predictions—0
360 Depth Estimation in the Wild -- The Depth360 Dataset and the SegFuse NetworkCode1
Automated Distance Estimation for Wildlife Camera TrappingCode1
HeadPosr: End-to-end Trainable Head Pose Estimation using Transformer Encoders—0
Transformers in Self-Supervised Monocular Depth Estimation with Unknown Camera IntrinsicsCode1
GLPanoDepth: Global-to-Local Panoramic Depth EstimationCode1
StandardSim: A Synthetic Dataset For Retail Environments—0
Scaling up Multi-domain Semantic Segmentation with Sentence Embeddings—0
Maximum Likelihood Uncertainty Estimation: Robustness to OutliersCode0
Towards 3D Scene Reconstruction from Locally Scale-Aligned Monocular Video Depth—0
PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth Estimation—0
Light field Rectification based on relative pose estimation—0
Unsupervised Single-shot Depth Estimation using Perceptual ReconstructionCode0
Mobile Robot Manipulation using Pure Object DetectionCode1
Comparison of Depth Estimation Setups from Stereo Endoscopy and Optical Tracking for Point Measurements—0
GeoFill: Reference-Based Image Inpainting with Better Geometric Understanding—0
A Confidence-based Iterative Solver of Depths and Surface Normals for Deep Multi-view StereoCode1
Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepthCode1
A Survey on RGB-D DatasetsCode2
Depth Estimation from Single-shot Monocular Endoscope Image Using Image Domain Adaptation And Edge-Aware Depth Estimation—0
MDS-Net: A Multi-scale Depth Stratification Based Monocular 3D Object Detection Algorithm—0
Maximizing Self-supervision from Thermal Image for Effective Self-supervised Learning of Depth and Ego-motionCode1
Multi-Robot Collaborative Perception with Graph Neural Networks—0
Robust photon-efficient imaging using a pixel-wise residual shrinkage networkCode1
Rethinking Depth Estimation for Multi-View Stereo: A Unified RepresentationCode2
Single-Stage Is Enough: Multi-Person Absolute 3D Pose Estimation—0
KeyTr: Keypoint Transporter for 3D Reconstruction of Deformable Objects in Videos—0
Exploiting Pseudo Labels in a Self-Supervised Learning Framework for Improved Monocular Depth Estimation—0
Generalizing Interactive Backpropagating Refinement for Dense Prediction Networks—0
Stereo Depth From Events Cameras: Concentrate and Focus on the FutureCode1
360MonoDepth: High-Resolution 360deg Monocular Depth EstimationCode2
Chitransformer: Towards Reliable Stereo From CuesCode1
Discrete Time Convolution for Fast Event-Based StereoCode1
Neural Window Fully-Connected CRFs for Monocular Depth Estimation—0
Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB ImagesCode0
Dense Depth Estimation from Multiple 360-degree Images Using Virtual DepthCode1
ACDNet: Adaptively Combined Dilated Convolution for Monocular Panorama Depth EstimationCode1
Depth estimation of endoscopy using sim-to-real transfer—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