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 1551–1600 of 2454 papers

TitleStatusHype
Blurry-Edges: Photon-Limited Depth Estimation from Defocused Boundaries—0
Bokeh Rendering Based on Adaptive Depth Calibration Network—0
Booster: a Benchmark for Depth from Images of Specular and Transparent Surfaces—0
Boosting Box-supervised Instance Segmentation with Pseudo Depth—0
Boosting Generalizability towards Zero-Shot Cross-Dataset Single-Image Indoor Depth by Meta-Initialization—0
Boosting Monocular 3D Object Detection with Object-Centric Auxiliary Depth Supervision—0
Boosting Monocular Depth Estimation with Lightweight 3D Point Fusion—0
Towards 3D Scene Reconstruction from Locally Scale-Aligned Monocular Video Depth—0
Boosting Self-Supervision for Single-View Scene Completion via Knowledge Distillation—0
Boosting Weakly Supervised Object Detection using Fusion and Priors from Hallucinated Depth—0
Boosting Zero-shot Stereo Matching using Large-scale Mixed Images Sources in the Real World—0
Bootstrapped Self-Supervised Training with Monocular Video for Semantic Segmentation and Depth Estimation—0
Boundary-guided Feature Aggregation Network for Salient Object Detection—0
BridgeNet: A Joint Learning Network of Depth Map Super-Resolution and Monocular Depth Estimation—0
Bridging Geometric and Semantic Foundation Models for Generalized Monocular Depth Estimation—0
BroadBEV: Collaborative LiDAR-camera Fusion for Broad-sighted Bird's Eye View Map Construction—0
BS3D: Building-scale 3D Reconstruction from RGB-D Images—0
Building Rome with Convex Optimization—0
BulletGen: Improving 4D Reconstruction with Bullet-Time Generation—0
ByDeWay: Boost Your multimodal LLM with DEpth prompting in a Training-Free Way—0
C2F2NeUS: Cascade Cost Frustum Fusion for High Fidelity and Generalizable Neural Surface Reconstruction—0
Calibrating Self-supervised Monocular Depth Estimation—0
Camera Height Doesn't Change: Unsupervised Training for Metric Monocular Road-Scene Depth Estimation—0
Camera-Only Bird's Eye View Perception: A Neural Approach to LiDAR-Free Environmental Mapping for Autonomous Vehicles—0
Cameras as Relative Positional Encoding—0
CamLessMonoDepth: Monocular Depth Estimation with Unknown Camera Parameters—0
Can Scale-Consistent Monocular Depth Be Learned in a Self-Supervised Scale-Invariant Manner?—0
Cascade Network for Self-Supervised Monocular Depth Estimation—0
Casual 6-DoF: free-viewpoint panorama using a handheld 360 camera—0
CatFree3D: Category-agnostic 3D Object Detection with Diffusion—0
CCDepth: A Lightweight Self-supervised Depth Estimation Network with Enhanced Interpretability—0
Center3D: Center-based Monocular 3D Object Detection with Joint Depth Understanding—0
CFDNet: A Generalizable Foggy Stereo Matching Network with Contrastive Feature Distillation—0
CH3Depth: Efficient and Flexible Depth Foundation Model with Flow Matching—0
CHOSEN: Contrastive Hypothesis Selection for Multi-View Depth Refinement—0
CI-Net: Contextual Information for Joint Semantic Segmentation and Depth Estimation—0
CLIFFNet for Monocular Depth Estimation with Hierarchical Embedding Loss—0
CLIP Can Understand Depth—0
CLIP meets Model Zoo Experts: Pseudo-Supervision for Visual Enhancement—0
CLIP with Quality Captions: A Strong Pretraining for Vision Tasks—0
CLONeR: Camera-Lidar Fusion for Occupancy Grid-aided Neural Representations—0
CodeMapping: Real-Time Dense Mapping for Sparse SLAM using Compact Scene Representations—0
CodeVIO: Visual-Inertial Odometry with Learned Optimizable Dense Depth—0
Coherence As Texture - Passive Textureless 3D Reconstruction by Self-interference—0
CoL3D: Collaborative Learning of Single-view Depth and Camera Intrinsics for Metric 3D Shape Recovery—0
ColDE: A Depth Estimation Framework for Colonoscopy Reconstruction—0
Colonoscopy 3D Video Dataset with Paired Depth from 2D-3D Registration—0
Comparison of Depth Estimation Setups from Stereo Endoscopy and Optical Tracking for Point Measurements—0
Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation—0
Composite Focus Measure for High Quality Depth Maps—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