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 1001–1025 of 2454 papers

TitleStatusHype
Towards Full-parameter and Parameter-efficient Self-learning For Endoscopic Camera Depth Estimation—0
CCDepth: A Lightweight Self-supervised Depth Estimation Network with Enhanced Interpretability—0
KineDepth: Utilizing Robot Kinematics for Online Metric Depth Estimation—0
fCOP: Focal Length Estimation from Category-level Object Priors—0
Self-supervised Monocular Depth Estimation with Large Kernel Attention—0
ViewpointDepth: A New Dataset for Monocular Depth Estimation Under Viewpoint Shifts—0
Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation—0
3DDX: Bone Surface Reconstruction from a Single Standard-Geometry Radiograph via Dual-Face Depth EstimationCode0
EventHDR: from Event to High-Speed HDR Videos and Beyond—0
Optical Lens Attack on Deep Learning Based Monocular Depth Estimation—0
Benchmarking Robustness of Endoscopic Depth Estimation with Synthetically Corrupted DataCode0
DepthART: Monocular Depth Estimation as Autoregressive Refinement Task—0
Robust and Flexible Omnidirectional Depth Estimation with Multiple 360° Cameras—0
UniBEVFusion: Unified Radar-Vision BEVFusion for 3D Object Detection—0
Generalizing monocular colonoscopy image depth estimation by uncertainty-based global and local fusion network—0
@Bench: Benchmarking Vision-Language Models for Human-centered Assistive Technology—0
SpaceBlender: Creating Context-Rich Collaborative Spaces Through Generative 3D Scene Blending—0
DAP-LED: Learning Degradation-Aware Priors with CLIP for Joint Low-light Enhancement and Deblurring—0
Interpretable Action Recognition on Hard to Classify Actions—0
Panoptic-Depth Forecasting—0
Depth Estimation Based on 3D Gaussian Splatting Siamese Defocus—0
GRIN: Zero-Shot Metric Depth with Pixel-Level Diffusion—0
Towards Single-Lens Controllable Depth-of-Field Imaging via Depth-Aware Point Spread FunctionsCode0
Precision Aquaculture: An Integrated Computer Vision and IoT Approach for Optimized Tilapia FeedingCode0
FIReStereo: Forest InfraRed Stereo Dataset for UAS Depth Perception in Visually Degraded Environments—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