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 1301–1350 of 2454 papers

TitleStatusHype
Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks—0
Colonoscopy 3D Video Dataset with Paired Depth from 2D-3D Registration—0
DenseMTL: Cross-task Attention Mechanism for Dense Multi-task LearningCode1
MonoGround: Detecting Monocular 3D Objects from the GroundCode1
LET-3D-AP: Longitudinal Error Tolerant 3D Average Precision for Camera-Only 3D DetectionCode4
TriHorn-Net: A Model for Accurate Depth-Based 3D Hand Pose EstimationCode1
Learning Ego 3D Representation as Ray TracingCode1
Depth Estimation Matters Most: Improving Per-Object Depth Estimation for Monocular 3D Detection and Tracking—0
Delving into the Pre-training Paradigm of Monocular 3D Object Detection—0
Dyna-DM: Dynamic Object-aware Self-supervised Monocular Depth MapsCode1
Unsupervised Learning of 3D Scene Flow from Monocular CameraCode0
Layered Depth Refinement with Mask Guidance—0
PanopticDepth: A Unified Framework for Depth-aware Panoptic SegmentationCode1
SMUDLP: Self-Teaching Multi-Frame Unsupervised Endoscopic Depth Estimation with Learnable Patchmatch—0
Self-Supervised Pre-training of Vision Transformers for Dense Prediction Tasks—0
RIAV-MVS: Recurrent-Indexing an Asymmetric Volume for Multi-View StereoCode0
Revealing the Dark Secrets of Masked Image ModelingCode1
Wavelet Feature Maps Compression for Image-to-Image CNNsCode1
Single-View View Synthesis in the Wild with Learned Adaptive Multiplane ImagesCode2
Deep Digging into the Generalization of Self-Supervised Monocular Depth EstimationCode1
UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes—0
Self-Supervised Depth Estimation with Isometric-Self-Sample-Based Learning—0
Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection—0
Achieving Risk Control in Online Learning SettingsCode0
Visual Attention-based Self-supervised Absolute Depth Estimation using Geometric Priors in Autonomous DrivingCode1
Positional Information is All You Need: A Novel Pipeline for Self-Supervised SVDE from Videos—0
Learning Monocular Depth Estimation via Selective Distillation of Stereo Knowledge—0
Efficient Stereo Depth Estimation for Pseudo LiDAR: A Self-Supervised Approach Based on Multi-Input ResNet Encoder—0
MulT: An End-to-End Multitask Learning TransformerCode1
Review on Panoramic Imaging and Its Applications in Scene Understanding—0
Is my Depth Ground-Truth Good Enough? HAMMER -- Highly Accurate Multi-Modal Dataset for DEnse 3D Scene Regression—0
Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representation—0
Non-parametric Depth Distribution Modelling based Depth Inference for Multi-view StereoCode1
FisheyeDistill: Self-Supervised Monocular Depth Estimation with Ordinal Distillation for Fisheye Cameras—0
Exploiting Correspondences with All-pairs Correlations for Multi-view Depth Estimation—0
Creating a Forensic Database of Shoeprints from Online Shoe Tread PhotosCode1
Outdoor Monocular Depth Estimation: A Research Review—0
MUTR3D: A Multi-camera Tracking Framework via 3D-to-2D QueriesCode2
Overcoming the Distance Estimation Bottleneck in Estimating Animal Abundance with Camera TrapsCode1
Unsupervised Visible-light Images Guided Cross-Spectrum Depth Estimation from Dual-Modality CamerasCode0
SideRT: A Real-time Pure Transformer Architecture for Single Image Depth Estimation—0
Depth Estimation with Simplified Transformer—0
Semi-MoreGAN: A New Semi-supervised Generative Adversarial Network for Mixture of Rain RemovalCode1
RealNet: Combining Optimized Object Detection with Information Fusion Depth Estimation Co-Design Method on IoTCode0
Investigating Neural Architectures by Synthetic Dataset DesignCode0
Monocular Depth Estimation Using Cues Inspired by Biological Vision SystemsCode0
Photometric single-view dense 3D reconstruction in endoscopyCode1
Cylin-Painting: Seamless 360 Panoramic Image Outpainting and BeyondCode1
UAMD-Net: A Unified Adaptive Multimodal Neural Network for Dense Depth Completion—0
Multi-Frame Self-Supervised Depth with Transformers—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