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 2001–2050 of 2454 papers

TitleStatusHype
How do Cross-View and Cross-Modal Alignment Affect Representations in Contrastive Learning?—0
How do neural networks see depth in single images?—0
How Much Depth Information can Radar Contribute to a Depth Estimation Model?—0
How to deal with glare for improved perception of Autonomous Vehicles—0
HRDFuse: Monocular 360deg Depth Estimation by Collaboratively Learning Holistic-With-Regional Depth Distributions—0
HRDFuse: Monocular 360°Depth Estimation by Collaboratively Learning Holistic-with-Regional Depth Distributions—0
HUSH: Holistic Panoramic 3D Scene Understanding using Spherical Harmonics—0
Hybrid Light Field Imaging for Improved Spatial Resolution and Depth Range—0
EndoPerfect: High-Accuracy Monocular Depth Estimation and 3D Reconstruction for Endoscopic Surgery via NeRF-Stereo Fusion—0
Hybridnet for depth estimation and semantic segmentation—0
Hybrid Skip: A Biologically Inspired Skip Connection for the UNet Architecture—0
Hybrid Transformer Based Feature Fusion for Self-Supervised Monocular Depth Estimation—0
I2P-Rec: Recognizing Images on Large-scale Point Cloud Maps through Bird's Eye View Projections—0
IAFA: Instance-aware Feature Aggregation for 3D Object Detection from a Single Image—0
ICG-MVSNet: Learning Intra-view and Cross-view Relationships for Guidance in Multi-View Stereo—0
iConFormer: Dynamic Parameter-Efficient Tuning with Input-Conditioned Adaptation—0
iELAS: An ELAS-Based Energy-Efficient Accelerator for Real-Time Stereo Matching on FPGA Platform—0
IFAST: Weakly Supervised Interpretable Face Anti-spoofing from Single-shot Binocular NIR Images—0
IGAF: Incremental Guided Attention Fusion for Depth Super-Resolution—0
Image and Depth from a Single Defocused Image Using Coded Aperture Photography—0
Image-to-Image Translation for Autonomous Driving from Coarsely-Aligned Image Pairs—0
Implicit and Explicit Language Guidance for Diffusion-based Visual Perception—0
Improved and efficient inter-vehicle distance estimation using road gradients of both ego and target vehicles—0
Improved Monocular Depth Prediction Using Distance Transform Over Pre-semantic Contours with Self-supervised Neural Networks—0
Improved Multiple-Image-Based Reflection Removal Algorithm Using Deep Neural Networks—0
Improved Neural Radiance Fields Using Pseudo-depth and Fusion—0
Improved Noise and Attack Robustness for Semantic Segmentation by Using Multi-Task Training with Self-Supervised Depth Estimation—0
Improving 2D face recognition via fine-level facial depth generation and RGB-D complementary feature learning—0
Improving 2D Feature Representations by 3D-Aware Fine-Tuning—0
Improving Bird's Eye View Semantic Segmentation by Task Decomposition—0
Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNN—0
Improving Distant 3D Object Detection Using 2D Box Supervision—0
Improving Domain Generalization in Self-supervised Monocular Depth Estimation via Stabilized Adversarial Training—0
Improving Geometric Consistency for 360-Degree Neural Radiance Fields in Indoor Scenarios—0
Improving Monocular Depth Estimation by Leveraging Structural Awareness and Complementary Datasets—0
Improving Monocular Visual Odometry Using Learned Depth—0
Improving Online Performance Prediction for Semantic Segmentation—0
Improving Pixel-Level Contrastive Learning by Leveraging Exogenous Depth Information—0
Improving the Reliability for Confidence Estimation—0
Increased-Range Unsupervised Monocular Depth Estimation—0
Incremental Joint Learning of Depth, Pose and Implicit Scene Representation on Monocular Camera in Large-scale Scenes—0
In defense of OSVOS—0
Independent Component Alignment for Multi-Task Learning—0
Indoor Layout Estimation by 2D LiDAR and Camera Fusion—0
Indoor Scene Structure Analysis for Single Image Depth Estimation—0
Inferring Point Clouds from Single Monocular Images by Depth Intermediation—0
Input-level Inductive Biases for 3D Reconstruction—0
InseRF: Text-Driven Generative Object Insertion in Neural 3D Scenes—0
In-situ and Non-contact Etch Depth Prediction in Plasma Etching via Machine Learning (ANN & BNN) and Digital Image Colorimetry—0
Instance-aware Multi-Camera 3D Object Detection with Structural Priors Mining and Self-Boosting Learning—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