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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 501–550 of 2454 papers

TitleStatusHype
Splatter a Video: Video Gaussian Representation for Versatile Processing—0
Depth Anywhere: Enhancing 360 Monocular Depth Estimation via Perspective Distillation and Unlabeled Data Augmentation—0
GeoBench: Benchmarking and Analyzing Monocular Geometry Estimation ModelsCode2
MEDeA: Multi-view Efficient Depth Adjustment—0
DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features—0
3D Gaze Tracking for Studying Collaborative Interactions in Mixed-Reality Environments—0
Self-supervised Pretraining and Finetuning for Monocular Depth and Visual Odometry—0
GenMM: Geometrically and Temporally Consistent Multimodal Data Generation for Video and LiDAR—0
The BabyView dataset: High-resolution egocentric videos of infants' and young children's everyday experiences—0
DurLAR: A High-fidelity 128-channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-modal Autonomous Driving ApplicationsCode2
D-NPC: Dynamic Neural Point Clouds for Non-Rigid View Synthesis from Monocular Video—0
Unsupervised Monocular Depth Estimation Based on Hierarchical Feature-Guided Diffusion—0
Depth Anything V2Code9
Multiple Prior Representation Learning for Self-Supervised Monocular Depth Estimation via Hybrid TransformerCode0
ToSA: Token Selective Attention for Efficient Vision Transformers—0
WonderWorld: Interactive 3D Scene Generation from a Single Image—0
Scale-Invariant Monocular Depth Estimation via SSI DepthCode1
Back to the Color: Learning Depth to Specific Color Transformation for Unsupervised Depth EstimationCode0
PLT-D3: A High-fidelity Dynamic Driving Simulation Dataset for Stereo Depth and Scene Flow—0
RS-DFM: A Remote Sensing Distributed Foundation Model for Diverse Downstream Tasks—0
PatchRefiner: Leveraging Synthetic Data for Real-Domain High-Resolution Monocular Metric Depth EstimationCode5
RefGaussian: Disentangling Reflections from 3D Gaussian Splatting for Realistic Rendering—0
Self-supervised Adversarial Training of Monocular Depth Estimation against Physical-World AttacksCode1
UVCPNet: A UAV-Vehicle Collaborative Perception Network for 3D Object Detection—0
Simplify Implant Depth Prediction as Video Grounding: A Texture Perceive Implant Depth Prediction Network—0
Deep Vision-Based Framework for Coastal Flood Prediction Under Climate Change Impacts and Shoreline AdaptationsCode0
Neural Surface Reconstruction from Sparse Views Using Epipolar Geometry—0
Flash3D: Feed-Forward Generalisable 3D Scene Reconstruction from a Single ImageCode3
MambaDepth: Enhancing Long-range Dependency for Self-Supervised Fine-Structured Monocular Depth Estimation—0
VHS: High-Resolution Iterative Stereo Matching with Visual Hull Priors—0
L-MAGIC: Language Model Assisted Generation of Images with CoherenceCode0
Learning Temporally Consistent Video Depth from Video Diffusion Priors—0
Self-Supervised Geometry-Guided Initialization for Robust Monocular Visual OdometryCode4
MoDGS: Dynamic Gaussian Splatting from Casually-captured Monocular Videos with Depth Priors—0
Uncertainty-guided Optimal Transport in Depth Supervised Sparse-View 3D Gaussian—0
Consistency Regularisation for Unsupervised Domain Adaptation in Monocular Depth EstimationCode0
DINO-SD: Champion Solution for ICRA 2024 RoboDepth Challenge—0
All-day Depth Completion—0
Benchmarking and Improving Bird's Eye View Perception Robustness in Autonomous DrivingCode3
DCPI-Depth: Explicitly Infusing Dense Correspondence Prior to Unsupervised Monocular Depth Estimation—0
ContrastAlign: Toward Robust BEV Feature Alignment via Contrastive Learning for Multi-Modal 3D Object Detection—0
GenWarp: Single Image to Novel Views with Semantic-Preserving Generative WarpingCode3
SDL-MVS: View Space and Depth Deformable Learning Paradigm for Multi-View Stereo Reconstruction in Remote Sensing—0
Estimating Depth of Monocular Panoramic Image with Teacher-Student Model Fusing Equirectangular and Spherical Representations—0
A Comparative Study on Multi-task Uncertainty Quantification in Semantic Segmentation and Monocular Depth Estimation—0
MonoDETRNext: Next-Generation Accurate and Efficient Monocular 3D Object Detector—0
Transparent Object Depth Completion—0
EvGGS: A Collaborative Learning Framework for Event-based Generalizable Gaussian SplattingCode1
Enhanced Object Tracking by Self-Supervised Auxiliary Depth Estimation Learning—0
Ghost-Stereo: GhostNet-based Cost Volume Enhancement and Aggregation for Stereo Matching Networks—0
Show:102550
← PrevPage 11 of 50Next →
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