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 101–150 of 2454 papers

TitleStatusHype
Monocular 3D Object Detection with Depth from MotionCode2
MonoCD: Monocular 3D Object Detection with Complementary DepthsCode2
MonoDGP: Monocular 3D Object Detection with Decoupled-Query and Geometry-Error PriorsCode2
Plane2Depth: Hierarchical Adaptive Plane Guidance for Monocular Depth EstimationCode2
Mono-ViFI: A Unified Learning Framework for Self-supervised Single- and Multi-frame Monocular Depth EstimationCode2
Neural Ray Surfaces for Self-Supervised Learning of Depth and Ego-motionCode2
Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth EstimationCode2
Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural NetworksCode2
Rethinking Depth Estimation for Multi-View Stereo: A Unified RepresentationCode2
360MonoDepth: High-Resolution 360deg Monocular Depth EstimationCode2
Learning to Recover 3D Scene Shape from a Single ImageCode2
Map-free Visual Relocalization: Metric Pose Relative to a Single ImageCode2
Self-Supervised Learning from Images with a Joint-Embedding Predictive ArchitectureCode2
Semantically-Guided Representation Learning for Self-Supervised Monocular DepthCode2
BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object DetectionCode2
Single-View View Synthesis in the Wild with Learned Adaptive Multiplane ImagesCode2
Sparse Auxiliary Networks for Unified Monocular Depth Prediction and CompletionCode2
SPIdepth: Strengthened Pose Information for Self-supervised Monocular Depth EstimationCode2
BEVStereo: Enhancing Depth Estimation in Multi-view 3D Object Detection with Dynamic Temporal StereoCode2
Know Your Neighbors: Improving Single-View Reconstruction via Spatial Vision-Language ReasoningCode2
Test3R: Learning to Reconstruct 3D at Test TimeCode2
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth EstimationCode2
Joint 2D-3D Multi-Task Learning on Cityscapes-3D: 3D Detection, Segmentation, and Depth EstimationCode2
Kick Back & Relax++: Scaling Beyond Ground-Truth Depth with SlowTV & CribsTVCode2
NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth EstimationCode2
HybridDepth: Robust Metric Depth Fusion by Leveraging Depth from Focus and Single-Image PriorsCode2
HoloTime: Taming Video Diffusion Models for Panoramic 4D Scene GenerationCode2
GeoMVSNet: Learning Multi-View Stereo With Geometry PerceptionCode2
GeoBench: Benchmarking and Analyzing Monocular Geometry Estimation ModelsCode2
GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for Real-time Human Novel View SynthesisCode2
IDOL: Unified Dual-Modal Latent Diffusion for Human-Centric Joint Video-Depth GenerationCode2
CVT-Occ: Cost Volume Temporal Fusion for 3D Occupancy PredictionCode2
Adaptive Fusion of Single-View and Multi-View Depth for Autonomous DrivingCode2
DDP: Diffusion Model for Dense Visual PredictionCode2
A Unified Image-Dense Annotation Generation Model for Underwater ScenesCode2
DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion ModelsCode2
ImOV3D: Learning Open-Vocabulary Point Clouds 3D Object Detection from Only 2D ImagesCode2
EndoDAC: Efficient Adapting Foundation Model for Self-Supervised Depth Estimation from Any Endoscopic CameraCode2
Atlas: End-to-End 3D Scene Reconstruction from Posed ImagesCode2
EA-LSS: Edge-aware Lift-splat-shot Framework for 3D BEV Object DetectionCode2
Enforcing geometric constraints of virtual normal for depth predictionCode2
Driv3R: Learning Dense 4D Reconstruction for Autonomous DrivingCode2
CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View CompletionCode2
DurLAR: A High-fidelity 128-channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-modal Autonomous Driving ApplicationsCode2
Event-based Stereo Depth Estimation: A SurveyCode2
Few-shot Novel View Synthesis using Depth Aware 3D Gaussian SplattingCode2
DiffusionDepth: Diffusion Denoising Approach for Monocular Depth EstimationCode2
Diffusion Models for Monocular Depth Estimation: Overcoming Challenging ConditionsCode2
A Simple Framework for 3D Occupancy Estimation in Autonomous DrivingCode2
Depth-Regularized Optimization for 3D Gaussian Splatting in Few-Shot ImagesCode2
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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