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 1101–1150 of 2454 papers

TitleStatusHype
WonderWorld: Interactive 3D Scene Generation from a Single Image—0
Multiple Prior Representation Learning for Self-Supervised Monocular Depth Estimation via Hybrid TransformerCode0
ToSA: Token Selective Attention for Efficient Vision Transformers—0
RS-DFM: A Remote Sensing Distributed Foundation Model for Diverse Downstream Tasks—0
PLT-D3: A High-fidelity Dynamic Driving Simulation Dataset for Stereo Depth and Scene Flow—0
Back to the Color: Learning Depth to Specific Color Transformation for Unsupervised Depth EstimationCode0
RefGaussian: Disentangling Reflections from 3D Gaussian Splatting for Realistic Rendering—0
Simplify Implant Depth Prediction as Video Grounding: A Texture Perceive Implant Depth Prediction Network—0
UVCPNet: A UAV-Vehicle Collaborative Perception Network for 3D Object Detection—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
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
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
All-day Depth Completion—0
DCPI-Depth: Explicitly Infusing Dense Correspondence Prior to Unsupervised Monocular Depth Estimation—0
A Comparative Study on Multi-task Uncertainty Quantification in Semantic Segmentation and Monocular Depth Estimation—0
Estimating Depth of Monocular Panoramic Image with Teacher-Student Model Fusing Equirectangular and Spherical Representations—0
ContrastAlign: Toward Robust BEV Feature Alignment via Contrastive Learning for Multi-Modal 3D Object Detection—0
Consistency Regularisation for Unsupervised Domain Adaptation in Monocular Depth EstimationCode0
DINO-SD: Champion Solution for ICRA 2024 RoboDepth Challenge—0
SDL-MVS: View Space and Depth Deformable Learning Paradigm for Multi-View Stereo Reconstruction in Remote Sensing—0
MonoDETRNext: Next-Generation Accurate and Efficient Monocular 3D Object Detector—0
Transparent Object Depth Completion—0
Ghost-Stereo: GhostNet-based Cost Volume Enhancement and Aggregation for Stereo Matching Networks—0
Enhanced Object Tracking by Self-Supervised Auxiliary Depth Estimation Learning—0
Cross-spectral Gated-RGB Stereo Depth Estimation—0
Depth Reconstruction with Neural Signed Distance Fields in Structured Light Systems—0
Depth Prompting for Sensor-Agnostic Depth EstimationCode0
CRF360D: Monocular 360 Depth Estimation via Spherical Fully-Connected CRFs—0
GEOcc: Geometrically Enhanced 3D Occupancy Network with Implicit-Explicit Depth Fusion and Contextual Self-Supervision—0
FA-Depth: Toward Fast and Accurate Self-supervised Monocular Depth EstimationCode0
Towards Task-Compatible Compressible RepresentationsCode0
KPNDepth: Depth Estimation of Lane Images under Complex Rainy Environment—0
The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition—0
CLIP with Quality Captions: A Strong Pretraining for Vision Tasks—0
SceneFactory: A Workflow-centric and Unified Framework for Incremental Scene Modeling—0
MGS-SLAM: Monocular Sparse Tracking and Gaussian Mapping with Depth Smooth Regularization—0
Ensuring UAV Safety: A Vision-only and Real-time Framework for Collision Avoidance Through Object Detection, Tracking, and Distance Estimation—0
A Construct-Optimize Approach to Sparse View Synthesis without Camera Pose—0
M^2Depth: Self-supervised Two-Frame Multi-camera Metric Depth Estimation—0
Domain-Transferred Synthetic Data Generation for Improving Monocular Depth Estimation—0
Depth Priors in Removal Neural Radiance Fields—0
Invisible Stitch: Generating Smooth 3D Scenes with Depth Inpainting—0
Tackling water table depth modeling via machine learning: From proxy observations to verifiabilityCode0
A Novel Spike Transformer Network for Depth Estimation from Event Cameras via Cross-modality Knowledge Distillation—0
Promoting CNNs with Cross-Architecture Knowledge Distillation for Efficient 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