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 2201–2250 of 2454 papers

TitleStatusHype
OmniDepth: Dense Depth Estimation for Indoors Spherical PanoramasCode0
Unsupervised object-centric video generation and decomposition in 3DCode0
Towards Single-Lens Controllable Depth-of-Field Imaging via Depth-Aware Point Spread FunctionsCode0
Octave Deep Plane-Sweeping Network: Reducing Spatial Redundancy for Learning-Based Plane-Sweeping StereoCode0
Occlusion-aware Unsupervised Learning of Depth from 4-D Light FieldsCode0
ObjCAViT: Improving Monocular Depth Estimation Using Natural Language Models And Image-Object Cross-AttentionCode0
Spherical View Synthesis for Self-Supervised 360 Depth EstimationCode0
FSA-Net: Learning Fine-Grained Structure Aggregation for Head Pose Estimation From a Single ImageCode0
Normal Assisted Stereo Depth EstimationCode0
Towards Task-Compatible Compressible RepresentationsCode0
3D Ken Burns Effect from a Single ImageCode0
Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB ImagesCode0
Dehazing Cost Volume for Deep Multi-view Stereo in Scattering Media with Airlight and Scattering Coefficient EstimationCode0
NimbleD: Enhancing Self-supervised Monocular Depth Estimation with Pseudo-labels and Large-scale Video Pre-trainingCode0
Focal-WNet: An Architecture Unifying Convolution and Attention for Depth EstimationCode0
NeSLAM: Neural Implicit Mapping and Self-Supervised Feature Tracking With Depth Completion and DenoisingCode0
Deep Vision-Based Framework for Coastal Flood Prediction Under Climate Change Impacts and Shoreline AdaptationsCode0
Bi-Real Net: Binarizing Deep Network Towards Real-Network PerformanceCode0
Adversarial Structure Matching for Structured Prediction TasksCode0
360SD-Net: 360° Stereo Depth Estimation with Learnable Cost VolumeCode0
Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance VotingCode0
Flow-Motion and Depth Network for Monocular Stereo and BeyondCode0
Revisiting Multi-Task Learning with ROCK: a Deep Residual Auxiliary Block for Visual DetectionCode0
Revisiting Self-Supervised Monocular Depth EstimationCode0
Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps with Accurate Object BoundariesCode0
MVDepthNet: Real-time Multiview Depth Estimation Neural NetworkCode0
Unsupervised Single Image Underwater Depth EstimationCode0
Mutli-View 3D Reconstruction using Knowledge DistillationCode0
DeepV2D: Video to Depth with Differentiable Structure from MotionCode0
RIAV-MVS: Recurrent-Indexing an Asymmetric Volume for Multi-View StereoCode0
Deep Coarse-to-fine Dense Light Field Reconstruction with Flexible Sampling and Geometry-aware FusionCode0
Multi-View Stereo by Temporal Nonparametric FusionCode0
Fast Scene Understanding for Autonomous DrivingCode0
DeepTAM: Deep Tracking and MappingCode0
Deep Neighbor Layer Aggregation for Lightweight Self-Supervised Monocular Depth EstimationCode0
Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object RepresentationCode0
Multiview Detection with Cardboard Human ModelingCode0
Multi-Task Meta Learning: learn how to adapt to unseen tasksCode0
Multi-task Learning for Monocular Depth and Defocus Estimations with Real ImagesCode0
RoboScape: Physics-informed Embodied World ModelCode0
DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene from Sparse LiDAR Data and Single Color ImageCode0
Deep Learning--Based Scene Simplification for Bionic VisionCode0
Unsupervised Single-shot Depth Estimation using Perceptual ReconstructionCode0
Fast Robust Monocular Depth Estimation for Obstacle Detection with Fully Convolutional NetworksCode0
Robust Consistent Video Depth EstimationCode0
Unsupervised Spike Depth Estimation via Cross-modality Cross-domain Knowledge TransferCode0
Adversarial Manhole: Challenging Monocular Depth Estimation and Semantic Segmentation Models with Patch AttackCode0
FastDepth: Fast Monocular Depth Estimation on Embedded SystemsCode0
Fast Depth Estimation for View SynthesisCode0
Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth EstimationCode0
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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