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 1001–1050 of 2454 papers

TitleStatusHype
Exploiting Depth from Single Monocular Images for Object Detection and Semantic Segmentation—0
H-Net: Unsupervised Attention-based Stereo Depth Estimation Leveraging Epipolar Geometry—0
A Framework for 3D Tracking of Frontal Dynamic Objects in Autonomous Cars—0
Exploiting Correspondences with All-pairs Correlations for Multi-view Depth Estimation—0
DATAP-SfM: Dynamic-Aware Tracking Any Point for Robust Structure from Motion in the Wild—0
Accurate and Real-time Pseudo Lidar Detection: Is Stereo Neural Network Really Necessary?—0
Exact Blur Measure Outperforms Conventional Learned Features for Depth Finding—0
Dense Depth Distillation with Out-of-Distribution Simulated Images—0
EvLight++: Low-Light Video Enhancement with an Event Camera: A Large-Scale Real-World Dataset, Novel Method, and More—0
How Much Depth Information can Radar Contribute to a Depth Estimation Model?—0
How to deal with glare for improved perception of Autonomous Vehicles—0
Depth by Poking: Learning to Estimate Depth from Self-Supervised Grasping—0
Data-Driven Method for Enhanced Corrosion Assessment of Reinforced Concrete Structures—0
HRDFuse: Monocular 360deg Depth Estimation by Collaboratively Learning Holistic-With-Regional Depth Distributions—0
Autonomously Navigating a Surgical Tool Inside the Eye by Learning from Demonstration—0
EvidMTL: Evidential Multi-Task Learning for Uncertainty-Aware Semantic Surface Mapping from Monocular RGB Images—0
EvidenceMoE: A Physics-Guided Mixture-of-Experts with Evidential Critics for Advancing Fluorescence Light Detection and Ranging in Scattering Media—0
HUSH: Holistic Panoramic 3D Scene Understanding using Spherical Harmonics—0
DarSwin-Unet: Distortion Aware Encoder-Decoder Architecture—0
Hybrid Light Field Imaging for Improved Spatial Resolution and Depth Range—0
EndoPerfect: High-Accuracy Monocular Depth Estimation and 3D Reconstruction for Endoscopic Surgery via NeRF-Stereo Fusion—0
Hybridnet for depth estimation and semantic segmentation—0
Every Pixel Counts: Unsupervised Geometry Learning with Holistic 3D Motion Understanding—0
DarSwin: Distortion Aware Radial Swin Transformer—0
Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies—0
Aerial Multi-View Stereo via Adaptive Depth Range Inference and Normal Cues—0
Large Language Models Can Understanding Depth from Monocular Images—0
Dark Channel-Assisted Depth-from-Defocus from a Single Image—0
Event Transformer+. A multi-purpose solution for efficient event data processing—0
Automated Floodwater Depth Estimation Using Large Multimodal Model for Rapid Flood Mapping—0
Event-Intensity Stereo: Estimating Depth by the Best of Both Worlds—0
EventHDR: from Event to High-Speed HDR Videos and Beyond—0
3D Densification for Multi-Map Monocular VSLAM in Endoscopy—0
Event Guided Depth Sensing—0
Event fields: Capturing light fields at high speed, resolution, and dynamic range—0
Event-based tracking of human hands—0
Event-Based Structured Light for Depth Reconstruction using Frequency Tagged Light Patterns—0
DAP-LED: Learning Degradation-Aware Priors with CLIP for Joint Low-light Enhancement and Deblurring—0
LAFFNet: A Lightweight Adaptive Feature Fusion Network for Underwater Image Enhancement—0
Event-based Monocular Dense Depth Estimation with Recurrent Transformers—0
EVEN: An Event-Based Framework for Monocular Depth Estimation at Adverse Night Conditions—0
AutoDepthNet: High Frame Rate Depth Map Reconstruction using Commodity Depth and RGB Cameras—0
DeepPerimeter: Indoor Boundary Estimation from Posed Monocular Sequences—0
EvConv: Fast CNN Inference on Event Camera Inputs For High-Speed Robot Perception—0
A Comparative Study on Multi-task Uncertainty Quantification in Semantic Segmentation and Monocular Depth Estimation—0
D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry—0
Evaluation of CNN-based Single-Image Depth Estimation Methods—0
Artificial Neural Network for Estimation of Physical Parameters of Sea Water using LiDAR Waveforms—0
A Dynamic Feature Interaction Framework for Multi-task Visual Perception—0
Language-Based Depth Hints for Monocular Depth Estimation—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