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 1551–1600 of 2454 papers

TitleStatusHype
Real-Time Monocular Human Depth Estimation and Segmentation on Embedded SystemsCode1
Bridging Unsupervised and Supervised Depth from Focus via All-in-Focus SupervisionCode1
SIDE: Center-based Stereo 3D Detector with Structure-aware Instance Depth Estimation—0
MobileStereoNet: Towards Lightweight Deep Networks for Stereo MatchingCode1
Fine-grained Semantics-aware Representation Enhancement for Self-supervised Monocular Depth EstimationCode1
VolumeFusion: Deep Depth Fusion for 3D Scene Reconstruction—0
StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimationCode1
A Simple Framework for 3D Lensless Imaging with Programmable MasksCode0
Panoramic Depth Estimation via Supervised and Unsupervised Learning in Indoor ScenesCode0
A Hybrid Sparse-Dense Monocular SLAM System for Autonomous DrivingCode1
Self-supervised Monocular Depth Estimation for All Day Images using Domain SeparationCode1
Is Pseudo-Lidar needed for Monocular 3D Object detection?Code1
DnD: Dense Depth Estimation in Crowded Dynamic Indoor Scenes—0
MultiTask-CenterNet (MCN): Efficient and Diverse Multitask Learning using an Anchor Free Approach—0
Towards Interpretable Deep Networks for Monocular Depth EstimationCode1
ConvNets vs. Transformers: Whose Visual Representations are More Transferable?—0
Improving Single-Image Defocus Deblurring: How Dual-Pixel Images Help Through Multi-Task LearningCode1
Multi-Source Fusion and Automatic Predictor Selection for Zero-Shot Video Object SegmentationCode1
R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes—0
UniNet: A Unified Scene Understanding Network and Exploring Multi-Task Relationships through the Lens of Adversarial AttacksCode0
Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the DarkCode1
Bird's-Eye-View Panoptic Segmentation Using Monocular Frontal View ImagesCode1
Visual Domain Adaptation for Monocular Depth Estimation on Resource-Constrained HardwareCode0
MFuseNet: Robust Depth Estimation with Learned Multiscopic Fusion—0
AcousticFusion: Fusing Sound Source Localization to Visual SLAM in Dynamic Environments—0
Consistent Depth of Moving Objects in Video—0
Pix2Point: Learning Outdoor 3D Using Sparse Point Clouds and Optimal Transport—0
Probabilistic and Geometric Depth: Detecting Objects in Perspective—0
RigNet: Repetitive Image Guided Network for Depth Completion—0
Geometry Uncertainty Projection Network for Monocular 3D Object DetectionCode1
Learning Geometry-Guided Depth via Projective Modeling for Monocular 3D Object DetectionCode1
CI-Net: Contextual Information for Joint Semantic Segmentation and Depth Estimation—0
Pseudo-LiDAR Based Road Detection—0
Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth—0
Unsupervised Monocular Depth Estimation in Highly Complex EnvironmentsCode1
BridgeNet: A Joint Learning Network of Depth Map Super-Resolution and Monocular Depth Estimation—0
MonoIndoor: Towards Good Practice of Self-Supervised Monocular Depth Estimation for Indoor Environments—0
Photon-Starved Scene Inference using Single Photon CamerasCode1
CodeMapping: Real-Time Dense Mapping for Sparse SLAM using Compact Scene Representations—0
CutDepth:Edge-aware Data Augmentation in Depth EstimationCode1
Depth Estimation from Monocular Images and Sparse radar using Deep Ordinal Regression NetworkCode1
MSFNet:Multi-scale features network for monocular depth estimation—0
MINERVAS: Massive INterior EnviRonments VirtuAl Synthesis—0
A Weakly-Supervised Depth Estimation Network Using Attention Mechanism—0
Self-Supervised Generative Adversarial Network for Depth Estimation in Laparoscopic Images—0
Edge-aware Bidirectional Diffusion for Dense Depth Estimation from Light FieldsCode1
Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance VotingCode0
TransformerFusion: Monocular RGB Scene Reconstruction using TransformersCode1
E-DSSR: Efficient Dynamic Surgical Scene Reconstruction with Transformer-based Stereoscopic Depth PerceptionCode1
Extraction of Key-frames of Endoscopic Videos by using Depth Information—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