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 801–850 of 2454 papers

TitleStatusHype
Automatic Discovery and Geotagging of Objects from Street View ImageryCode0
On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network ApproachCode0
On the Robustness of Language Guidance for Low-Level Vision Tasks: Findings from Depth EstimationCode0
AutoColor: Learned Light Power Control for Multi-Color HologramsCode0
D4D: An RGBD diffusion model to boost monocular depth estimationCode0
On the Benefit of Adversarial Training for Monocular Depth EstimationCode0
D^3epth: Self-Supervised Depth Estimation with Dynamic Mask in Dynamic ScenesCode0
On Robust Cross-View Consistency in Self-Supervised Monocular Depth EstimationCode0
Point Spread Function Estimation of DefocusCode0
Precision Aquaculture: An Integrated Computer Vision and IoT Approach for Optimized Tilapia FeedingCode0
P3P: Pseudo-3D Pre-training for Scaling 3D Voxel-based Masked AutoencodersCode0
Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric AnnotationsCode0
Octave Deep Plane-Sweeping Network: Reducing Spatial Redundancy for Learning-Based Plane-Sweeping StereoCode0
Estimating Depth from RGB and Sparse SensingCode0
Estimated Depth Map Helps Image ClassificationCode0
EPP-MVSNet: Epipolar-Assembling Based Depth Prediction for Multi-View StereoCode0
CVCP-Fusion: On Implicit Depth Estimation for 3D Bounding Box PredictionCode0
OmniDepth: Dense Depth Estimation for Indoors Spherical PanoramasCode0
EPINET: A Fully-Convolutional Neural Network Using Epipolar Geometry for Depth from Light Field ImagesCode0
Cut-and-Splat: Leveraging Gaussian Splatting for Synthetic Data GenerationCode0
Fast Scene Understanding for Autonomous DrivingCode0
PU-Ray: Domain-Independent Point Cloud Upsampling via Ray Marching on Neural Implicit SurfaceCode0
Occlusion-aware Unsupervised Learning of Depth from 4-D Light FieldsCode0
Enhancing Underwater Imaging with 4-D Light Fields: Dataset and MethodCode0
OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous DrivingCode0
Enhancing Monocular Depth Estimation with Multi-Source Auxiliary TasksCode0
NimbleD: Enhancing Self-supervised Monocular Depth Estimation with Pseudo-labels and Large-scale Video Pre-trainingCode0
Adversarial Structure Matching for Structured Prediction TasksCode0
Normal Assisted Stereo Depth EstimationCode0
AttEntropy: On the Generalization Ability of Supervised Semantic Segmentation Transformers to New Objects in New DomainsCode0
NeSLAM: Neural Implicit Mapping and Self-Supervised Feature Tracking With Depth Completion and DenoisingCode0
360SD-Net: 360° Stereo Depth Estimation with Learnable Cost VolumeCode0
ObjCAViT: Improving Monocular Depth Estimation Using Natural Language Models And Image-Object Cross-AttentionCode0
EndoLRMGS: Complete Endoscopic Scene Reconstruction combining Large Reconstruction Modelling and Gaussian SplattingCode0
EndoGaussian: Real-time Gaussian Splatting for Dynamic Endoscopic Scene ReconstructionCode0
MVDepthNet: Real-time Multiview Depth Estimation Neural NetworkCode0
Creative Flow+ DatasetCode0
Adversarial Manhole: Challenging Monocular Depth Estimation and Semantic Segmentation Models with Patch AttackCode0
Mutli-View 3D Reconstruction using Knowledge DistillationCode0
Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic ImageryCode0
Multiview Detection with Cardboard Human ModelingCode0
Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object RepresentationCode0
EgoDTM: Towards 3D-Aware Egocentric Video-Language PretrainingCode0
Multi-Task Meta Learning: learn how to adapt to unseen tasksCode0
Attention-Based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object DetectionCode0
Multi-View Stereo by Temporal Nonparametric FusionCode0
Co-SemDepth: Fast Joint Semantic Segmentation and Depth Estimation on Aerial ImagesCode0
Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth EstimationCode0
Deep Coarse-to-fine Dense Light Field Reconstruction with Flexible Sampling and Geometry-aware FusionCode0
Correlation of Object Detection Performance with Visual Saliency and 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