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

Showing 19512000 of 2454 papers

TitleStatusHype
Improving Monocular Depth Estimation by Leveraging Structural Awareness and Complementary Datasets0
Mobile3DRecon: Real-time Monocular 3D Reconstruction on a Mobile Phone0
Object-Aware Centroid Voting for Monocular 3D Object Detection0
People as Scene Probes0
Partially Supervised Multi-Task Network for Single-View Dietary Assessment0
P2D: a self-supervised method for depth estimation from polarimetry0
Monocular Retinal Depth Estimation and Joint Optic Disc and Cup Segmentation using Adversarial Networks0
UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a Generic Framework for Handling Common Camera Distortion Models0
Unsupervised object-centric video generation and decomposition in 3DCode0
Self-supervised Depth Estimation to Regularise Semantic Segmentation in Knee Arthroscopy0
Synergistic saliency and depth prediction for RGB-D saliency detection0
Data-Driven Method for Enhanced Corrosion Assessment of Reinforced Concrete Structures0
Joint Hand-object 3D Reconstruction from a Single Image with Cross-branch Feature Fusion0
MiniNet: An extremely lightweight convolutional neural network for real-time unsupervised monocular depth estimation0
An Advert Creation System for 3D Product Placements0
Increased-Range Unsupervised Monocular Depth Estimation0
Self-Supervised Joint Learning Framework of Depth Estimation via Implicit Cues0
AcED: Accurate and Edge-consistent Monocular Depth Estimation0
Depth by Poking: Learning to Estimate Depth from Self-Supervised Grasping0
Attentive Feature Reuse for Multi Task Meta learning0
Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies0
Semantics-Driven Unsupervised Learning for Monocular Depth and Ego-Motion Estimation0
DeepRelativeFusion: Dense Monocular SLAM using Single-Image Relative Depth Prediction0
Content-Aware Inter-Scale Cost Aggregation for Stereo Matching0
FP-Stereo: Hardware-Efficient Stereo Vision for Embedded Applications0
From Real to Synthetic and Back: Synthesizing Training Data for Multi-Person Scene Understanding0
PLG-IN: Pluggable Geometric Consistency Loss with Wasserstein Distance in Monocular Depth Estimation0
SDC-Depth: Semantic Divide-and-Conquer Network for Monocular Depth Estimation0
A Survey on Deep Learning Techniques for Stereo-based Depth Estimation0
Attention-Aware Multi-View Stereo0
Disparity-Aware Domain Adaptation in Stereo Image Restoration0
VPLNet: Deep Single View Normal Estimation With Vanishing Points and Lines0
Pattern-Structure Diffusion for Multi-Task Learning0
Fusion of Real Time Thermal Image and 1D/2D/3D Depth Laser Readings for Remote Thermal Sensing in Industrial Plants by Means of UAVs and/or Robots0
Online Depth Learning Against Forgetting in Monocular Videos0
Geometric Structure Based and Regularized Depth Estimation From 360 Indoor Imagery0
Monocular Depth Estimators: Vulnerabilities and Attacks0
Self-Attention Dense Depth Estimation Network for Unrectified Video Sequences0
Center3D: Center-based Monocular 3D Object Detection with Joint Depth Understanding0
Visual Localization Using Semantic Segmentation and Depth Prediction0
Decoder Modulation for Indoor Depth Completion0
Deep feature fusion for self-supervised monocular depth prediction0
Exploring the Capabilities and Limits of 3D Monocular Object Detection -- A Study on Simulation and Real World Data0
Taskology: Utilizing Task Relations at Scale0
VisualEchoes: Spatial Image Representation Learning through Echolocation0
Deep 3D Pan via Local adaptive "t-shaped" convolutions with global and local adaptive dilations0
Deflating Dataset Bias Using Synthetic Data Augmentation0
Self-Supervised Attention Learning for Depth and Ego-motion Estimation0
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP0
Learning to Autofocus0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1OmniDepthRMSE0.62Unverified
2SphereDepthRMSE0.45Unverified
3Jin et al.RMSE0.42Unverified
4BiFuse with fusionRMSE0.41Unverified
5HoHoNet (ResNet-101)RMSE0.38Unverified
6PanoDepthRMSE0.37Unverified
7BiFuse++RMSE0.37Unverified
8UniFuse with fusionRMSE0.37Unverified
9DisConvRMSE0.37Unverified
10SliceNetRMSE0.37Unverified
#ModelMetricClaimedVerifiedStatus
1A2JmAP8.61Unverified
2PAD-NetRMS0.79Unverified
3MS-CRFRMS0.59Unverified
4DORNRMS0.51Unverified
5FreeformRMS0.43Unverified
6Optimized, freeformRMS0.43Unverified
7VNLRMS0.42Unverified
8BTSRMS0.41Unverified
9TransDepth (AGD+ ViT)RMS0.37Unverified
10AdaBinsRMS0.36Unverified
#ModelMetricClaimedVerifiedStatus
1T2NetAbs Rel0.35Unverified
2MIDASAbs Rel0.31Unverified
3Bhattacharjee et al.Abs Rel0.25Unverified
#ModelMetricClaimedVerifiedStatus
1T2NetAbs Rel0.49Unverified
2MIDASAbs Rel0.42Unverified
3Bhattacharjee et al.Abs Rel0.38Unverified
#ModelMetricClaimedVerifiedStatus
1LeReSabsolute relative error0.1Unverified
2DELTASabsolute relative error0.09Unverified
3Distill Any Depthabsolute relative error0.04Unverified
#ModelMetricClaimedVerifiedStatus
1SDC-DepthRMSE6.92Unverified
2SwinMTLRMSE6.35Unverified
#ModelMetricClaimedVerifiedStatus
1AIP-BrownDelta < 1.250.36Unverified
2LeResDelta < 1.250.23Unverified
#ModelMetricClaimedVerifiedStatus
1H-Net (Ours)Absolute relative error (AbsRel)0.09Unverified
2H-Net (Ours) Full EigenAbsolute relative error (AbsRel)0.08Unverified
#ModelMetricClaimedVerifiedStatus
1GLPDepthDelta < 1.250.43Unverified
2SRDINET (Model A)Delta < 1.250.4Unverified
#ModelMetricClaimedVerifiedStatus
1Atlas (finetuned)RMSE0.17Unverified
2Atlas (plain)RMSE0.17Unverified
#ModelMetricClaimedVerifiedStatus
1LFattNetBadPix(0.01)17.23Unverified
#ModelMetricClaimedVerifiedStatus
1LightDepthNumber of parameters (M)42.6Unverified
#ModelMetricClaimedVerifiedStatus
1UniFuseAbs Rel0.11Unverified
#ModelMetricClaimedVerifiedStatus
1X-TC (Cross-Task Consistency)L1 error1.63Unverified