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 1501–1550 of 2454 papers

TitleStatusHype
CamLessMonoDepth: Monocular Depth Estimation with Unknown Camera Parameters—0
X-Distill: Improving Self-Supervised Monocular Depth via Cross-Task Distillation—0
Pseudo Supervised Monocular Depth Estimation with Teacher-Student Network—0
Self-Supervised Monocular Scene Decomposition and Depth Estimation—0
PlaneRecNet: Multi-Task Learning with Cross-Task Consistency for Piece-Wise Plane Detection and Reconstruction from a Single RGB ImageCode1
Depth360: Self-supervised Learning for Monocular Depth Estimation using Learnable Camera Distortion Model—0
Event Guided Depth Sensing—0
Self-Supervised Monocular Depth Estimation with Internal Feature FusionCode1
Attention meets Geometry: Geometry Guided Spatial-Temporal Attention for Consistent Self-Supervised Monocular Depth Estimation—0
Semi-supervised Multi-task Learning for Semantics and Depth—0
Task-Driven Deep Image Enhancement Network for Autonomous Driving in Bad Weather—0
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation—0
Plugging Self-Supervised Monocular Depth into Unsupervised Domain Adaptation for Semantic SegmentationCode0
Non-local Recurrent Regularization Networks for Multi-view Stereo—0
DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D QueriesCode1
Monocular Depth Estimation with Sharp Boundary—0
PLNet: Plane and Line Priors for Unsupervised Indoor Depth EstimationCode1
Stereo Hybrid Event-Frame (SHEF) Cameras for 3D PerceptionCode1
Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D ScansCode1
Modality-Guided Subnetwork for Salient Object Detection—0
Stereo Dense Scene Reconstruction and Accurate Localization for Learning-Based Navigation of Laparoscope in Minimally Invasive Surgery—0
DoubleStar: Long-Range Attack Towards Depth Estimation based Obstacle Avoidance in Autonomous Systems—0
Estimating Image Depth in the Comics DomainCode1
See Yourself in Others: Attending Multiple Tasks for Own Failure Detection—0
Echo-Reconstruction: Audio-Augmented 3D Scene Reconstruction—0
D-Net: A Generalised and Optimised Deep Network for Monocular Depth EstimationCode0
Wavelet Feature Maps Compression for Low Bandwidth Convolutional Neural Networks—0
Contrastive Mutual Information Maximization for Binary Neural Networks—0
StereoSpike: Depth Learning with a Spiking Neural NetworkCode1
f-Cal: Calibrated aleatoric uncertainty estimation from neural networks for robot perception—0
Excavating the Potential Capacity of Self-Supervised Monocular Depth EstimationCode1
Adversarial Domain Feature Adaptation for Bronchoscopic Depth Estimation—0
Weakly-Supervised Monocular Depth Estimationwith Resolution-Mismatched Data—0
Improving 360 Monocular Depth Estimation via Non-local Dense Prediction Transformer and Joint Supervised and Self-supervised LearningCode1
Survey on Semantic Stereo Matching / Semantic Depth Estimation—0
Scale-aware direct monocular odometry—0
Augmenting Depth Estimation with Geospatial Context—0
Advancing Self-supervised Monocular Depth Learning with Sparse LiDARCode1
On the Sins of Image Synthesis Loss for Self-supervised Depth Estimation—0
RVMDE: Radar Validated Monocular Depth Estimation for RoboticsCode1
LiDARTouch: Monocular metric depth estimation with a few-beam LiDARCode1
Pano3D: A Holistic Benchmark and a Solid Baseline for 360^o Depth EstimationCode1
Light Field-Based Underwater 3D Reconstruction Via Angular Resampling—0
Dual Transfer Learning for Event-based End-task Prediction via Pluggable Event to Image TranslationCode1
NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view StereoCode1
Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth EstimationCode1
DenseLiDAR: A Real-Time Pseudo Dense Depth Guided Depth Completion Network—0
Multi-task learning from fixed-wing UAV images for 2D/3D city modeling—0
Lightweight Monocular Depth with a Novel Neural Architecture Search Method—0
Monocular Depth Estimation Primed by Salient Point Detection and Normalized Hessian Loss—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