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 751–800 of 2454 papers

TitleStatusHype
Progressive Fusion for Unsupervised Binocular Depth Estimation using Cycled NetworksCode0
Achievement-Based Training Progress Balancing for Multi-Task LearningCode0
Benchmarking Robust Self-Supervised Learning Across Diverse Downstream TasksCode0
Deep Learning--Based Scene Simplification for Bionic VisionCode0
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous DrivingCode0
Benchmarking Robustness of Endoscopic Depth Estimation with Synthetically Corrupted DataCode0
Pose Constraints for Consistent Self-supervised Monocular Depth and Ego-motionCode0
Point Spread Function Estimation of DefocusCode0
3DDX: Bone Surface Reconstruction from a Single Standard-Geometry Radiograph via Dual-Face Depth EstimationCode0
Deep Depth from Defocus: how can defocus blur improve 3D estimation using dense neural networks?Code0
Precision Aquaculture: An Integrated Computer Vision and IoT Approach for Optimized Tilapia FeedingCode0
Deep Depth Estimation From Thermal ImageCode0
Plug-and-Play: Improve Depth Estimation via Sparse Data PropagationCode0
Deep Depth Completion of a Single RGB-D ImageCode0
PlaneNet: Piece-wise Planar Reconstruction from a Single RGB ImageCode0
Deep Component Analysis via Alternating Direction Neural NetworksCode0
Plugging Self-Supervised Monocular Depth into Unsupervised Domain Adaptation for Semantic SegmentationCode0
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional ArchitectureCode0
PID4LaTe: a physics-informed deep learning model for lake multi-depth temperature predictionCode0
Deep attention-based classification network for robust depth predictionCode0
Pixel-Accurate Depth Evaluation in Realistic Driving ScenariosCode0
Back to the Color: Learning Depth to Specific Color Transformation for Unsupervised Depth EstimationCode0
PLADE-Net: Towards Pixel-Level Accuracy for Self-Supervised Single-View Depth Estimation with Neural Positional Encoding and Distilled Matting LossCode0
Panoramic Depth Estimation via Supervised and Unsupervised Learning in Indoor ScenesCode0
Parse Geometry from a Line: Monocular Depth Estimation with Partial Laser ObservationCode0
P3P: Pseudo-3D Pre-training for Scaling 3D Voxel-based Masked AutoencodersCode0
PhaseCam3D — Learning Phase Masks for Passive Single View Depth EstimationCode0
Planar Prior Assisted PatchMatch Multi-View StereoCode0
Exploiting temporal consistency for real-time video depth estimationCode0
On the Robustness of Language Guidance for Low-Level Vision Tasks: Findings from Depth EstimationCode0
Exploiting Depth Information for Wildlife MonitoringCode0
Auxiliary Tasks in Multi-task LearningCode0
On the Benefit of Adversarial Training for Monocular Depth EstimationCode0
On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network ApproachCode0
On Robust Cross-View Consistency in Self-Supervised Monocular Depth EstimationCode0
OmniMVS: End-to-End Learning for Omnidirectional Stereo MatchingCode0
Absolute Human Pose Estimation with Depth Prediction NetworkCode0
OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous DrivingCode0
Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic UnderstandingCode0
Automatic Discovery and Geotagging of Objects from Street View ImageryCode0
Occlusion-aware Unsupervised Learning of Depth from 4-D Light FieldsCode0
Octave Deep Plane-Sweeping Network: Reducing Spatial Redundancy for Learning-Based Plane-Sweeping StereoCode0
AutoColor: Learned Light Power Control for Multi-Color HologramsCode0
D4D: An RGBD diffusion model to boost monocular depth estimationCode0
Normal Assisted Stereo Depth EstimationCode0
D^3epth: Self-Supervised Depth Estimation with Dynamic Mask in Dynamic ScenesCode0
NimbleD: Enhancing Self-supervised Monocular Depth Estimation with Pseudo-labels and Large-scale Video Pre-trainingCode0
ObjCAViT: Improving Monocular Depth Estimation Using Natural Language Models And Image-Object Cross-AttentionCode0
OmniDepth: Dense Depth Estimation for Indoors Spherical PanoramasCode0
Dense Depth Estimation in Monocular Endoscopy with Self-supervised Learning MethodsCode0
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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