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 651–700 of 2454 papers

TitleStatusHype
Height and Uprightness Invariance for 3D Prediction From a Single ViewCode1
Why Having 10,000 Parameters in Your Camera Model Is Better Than TwelveCode1
IDA-3D: Instance-Depth-Aware 3D Object Detection From Stereo Vision for Autonomous DrivingCode1
Focus on defocus: bridging the synthetic to real domain gap for depth estimationCode1
Bi3D: Stereo Depth Estimation via Binary ClassificationsCode1
On the uncertainty of self-supervised monocular depth estimationCode1
Self-Supervised Human Depth Estimation from Monocular VideosCode1
Toward Hierarchical Self-Supervised Monocular Absolute Depth Estimation for Autonomous Driving ApplicationsCode1
Self-Supervised Monocular Scene Flow EstimationCode1
End-to-End Pseudo-LiDAR for Image-Based 3D Object DetectionCode1
Guiding Monocular Depth Estimation Using Depth-Attention VolumeCode1
Towards Better Generalization: Joint Depth-Pose Learning without PoseNetCode1
Occlusion-Aware Depth Estimation with Adaptive Normal ConstraintsCode1
The Edge of Depth: Explicit Constraints between Segmentation and DepthCode1
Self-supervised Monocular Trained Depth Estimation using Self-attention and Discrete Disparity VolumeCode1
Distilled Semantics for Comprehensive Scene Understanding from VideosCode1
DeFeat-Net: General Monocular Depth via Simultaneous Unsupervised Representation LearningCode1
Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton RefinementCode1
Holopix50k: A Large-Scale In-the-wild Stereo Image DatasetCode1
Monocular Depth Prediction through Continuous 3D LossCode1
DELTAS: Depth Estimation by Learning Triangulation And densification of Sparse pointsCode1
Softmax Splatting for Video Frame InterpolationCode1
A-TVSNet: Aggregated Two-View Stereo Network for Multi-View Stereo Depth EstimationCode1
Unsupervised Learning of Depth, Optical Flow and Pose with Occlusion from 3D GeometryCode1
Predicting Sharp and Accurate Occlusion Boundaries in Monocular Depth Estimation Using Displacement FieldsCode1
Learning Light Field Angular Super-Resolution via a Geometry-Aware NetworkCode1
Attention-based View Selection Networks for Light-field Disparity EstimationCode1
DiverseDepth: Affine-invariant Depth Prediction Using Diverse DataCode1
Active Perception with A Monocular Camera for Multiscopic VisionCode1
Single Image Depth Estimation Trained via Depth from Defocus CuesCode1
Understanding Contrastive Representation Learning through Geometry on the HypersphereCode1
Why Having 10,000 Parameters in Your Camera Model is Better Than TwelveCode1
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
From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth EstimationCode1
UnOS: Unified Unsupervised Optical-Flow and Stereo-Depth Estimation by Watching VideosCode1
3D Packing for Self-Supervised Monocular Depth EstimationCode1
Unsupervised Learning of Depth and Ego-Motion from Cylindrical Panoramic VideoCode1
High Quality Monocular Depth Estimation via Transfer LearningCode1
Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous DrivingCode1
Efficient Attention: Attention with Linear ComplexitiesCode1
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary CellsCode1
Towards real-time unsupervised monocular depth estimation on CPUCode1
Deep Ordinal Regression Network for Monocular Depth EstimationCode1
Digging Into Self-Supervised Monocular Depth EstimationCode1
GeoNet: Geometric Neural Network for Joint Depth and Surface Normal EstimationCode1
Real-Time Monocular Depth Estimation Using Synthetic Data With Domain Adaptation via Image Style TransferCode1
MegaDepth: Learning Single-View Depth Prediction from Internet PhotosCode1
Pyramid Stereo Matching NetworkCode1
Generalizable Data-free Objective for Crafting Universal Adversarial PerturbationsCode1
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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