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 2176–2200 of 2454 papers

TitleStatusHype
Synthesizing a 4D Spatio-Angular Consistent Light Field from a Single Image—0
Veritatem Dies Aperit- Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding ApproachCode0
Low Power Depth Estimation of Rigid Objects for Time-of-Flight Imaging—0
Monocular 3D Object Detection with Pseudo-LiDAR Point CloudCode0
Sparse2Dense: From direct sparse odometry to dense 3D reconstruction—0
A Novel Monocular Disparity Estimation Network with Domain Transformation and Ambiguity Learning—0
Bilateral Cyclic Constraint and Adaptive Regularization for Unsupervised Monocular Depth PredictionCode0
DFineNet: Ego-Motion Estimation and Depth Refinement from Sparse, Noisy Depth Input with RGB Guidance—0
Refine and Distill: Exploiting Cycle-Inconsistency and Knowledge Distillation for Unsupervised Monocular Depth Estimation—0
Structured Knowledge Distillation for Dense PredictionCode0
A Unified Formulation for Visual Odometry—0
FastDepth: Fast Monocular Depth Estimation on Embedded SystemsCode0
Self-supervised Learning for Single View Depth and Surface Normal Estimation—0
SweepNet: Wide-baseline Omnidirectional Depth EstimationCode0
Region Deformer Networks for Unsupervised Depth Estimation from Unconstrained Monocular VideosCode0
Dead Time Compensation for High-Flux Ranging—0
Dense Depth Estimation in Monocular Endoscopy with Self-supervised Learning MethodsCode0
Sparse and noisy LiDAR completion with RGB guidance and uncertaintyCode0
Sparse and noisy LiDAR completion with RGB guidance anduncertaintyCode0
Dense Depth Estimation of a Complex Dynamic Scene without Explicit 3D Motion Estimation—0
NeurAll: Towards a Unified Visual Perception Model for Automated Driving—0
GEN-SLAM: Generative Modeling for Monocular Simultaneous Localization and Mapping—0
Unstructured Multi-View Depth Estimation Using Mask-Based Multiplane RepresentationCode0
Fully Convolutional Networks for Monocular Retinal Depth Estimation and Optic Disc-Cup Segmentation—0
Attention-based Context Aggregation Network for Monocular Depth EstimationCode0
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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