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 1101–1150 of 2454 papers

TitleStatusHype
Energy-Efficient Adaptive 3D SensingCode1
NeuralLift-360: Lifting an In-the-Wild 2D Photo to a 3D Object With 360deg Views—0
OmniVidar: Omnidirectional Depth Estimation From Multi-Fisheye ImagesCode1
Depth Estimation From Camera Image and mmWave Radar Point Cloud—0
AttEntropy: On the Generalization Ability of Supervised Semantic Segmentation Transformers to New Objects in New DomainsCode0
HandsOff: Labeled Dataset Generation With No Additional Human Annotations—0
Shakes on a Plane: Unsupervised Depth Estimation from Unstabilized Photography—0
Vision-Based Environmental Perception for Autonomous Driving—0
Depth Estimation maps of lidar and stereo images—0
MaskingDepth: Masked Consistency Regularization for Semi-supervised Monocular Depth EstimationCode1
Lightweight Monocular Depth Estimation—0
Scene-aware Egocentric 3D Human Pose EstimationCode1
DAG: Depth-Aware Guidance with Denoising Diffusion Probabilistic ModelsCode1
Are We Ready for Vision-Centric Driving Streaming Perception? The ASAP BenchmarkCode1
Solve the Puzzle of Instance Segmentation in Videos: A Weakly Supervised Framework with Spatio-Temporal Collaboration—0
Towards Practical Plug-and-Play Diffusion ModelsCode1
ROIFormer: Semantic-Aware Region of Interest Transformer for Efficient Self-Supervised Monocular Depth Estimation—0
Mind The Edge: Refining Depth Edges in Sparsely-Supervised Monocular Depth EstimationCode1
Cross-Domain Synthetic-to-Real In-the-Wild Depth and Normal Estimation for 3D Scene Understanding—0
Objects as Spatio-Temporal 2.5D points—0
Event-based Monocular Dense Depth Estimation with Recurrent Transformers—0
GARF:Geometry-Aware Generalized Neural Radiance Field—0
SceneRF: Self-Supervised Monocular 3D Scene Reconstruction with Radiance FieldsCode2
3D Object Aided Self-Supervised Monocular Depth Estimation—0
Multi-resolution Monocular Depth Map Fusion by Self-supervised Gradient-based CompositionCode1
Geometry-Aware Network for Domain Adaptive Semantic Segmentation—0
BEV-LGKD: A Unified LiDAR-Guided Knowledge Distillation Framework for BEV 3D Object DetectionCode1
Weakly Supervised 3D Multi-person Pose Estimation for Large-scale Scenes based on Monocular Camera and Single LiDAR—0
BEVUDA: Multi-geometric Space Alignments for Domain Adaptive BEV 3D Object Detection—0
ObjCAViT: Improving Monocular Depth Estimation Using Natural Language Models And Image-Object Cross-AttentionCode0
Attention-Based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object DetectionCode0
Self-Supervised Surround-View Depth Estimation with Volumetric Feature FusionCode1
Generalized Face Anti-Spoofing via Multi-Task Learning and One-Side Meta Triplet Loss—0
SuperFusion: Multilevel LiDAR-Camera Fusion for Long-Range HD Map GenerationCode2
3DPPE: 3D Point Positional Encoding for Multi-Camera 3D Object Detection TransformersCode1
Copy-Pasting Coherent Depth Regions Improves Contrastive Learning for Urban-Scene SegmentationCode0
How do Cross-View and Cross-Modal Alignment Affect Representations in Contrastive Learning?—0
Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth EstimationCode2
The Monocular Depth Estimation ChallengeCode1
Event Transformer+. A multi-purpose solution for efficient event data processing—0
AeDet: Azimuth-invariant Multi-view 3D Object DetectionCode1
MSS-DepthNet: Depth Prediction with Multi-Step Spiking Neural Network—0
Dynamic Depth-Supervised NeRF for Multi-View RGB-D Operating Room Images—0
DesNet: Decomposed Scale-Consistent Network for Unsupervised Depth Completion—0
Hybrid Transformer Based Feature Fusion for Self-Supervised Monocular Depth Estimation—0
A Practical Stereo Depth System for Smart GlassesCode1
Improving Pixel-Level Contrastive Learning by Leveraging Exogenous Depth Information—0
BEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object DetectionCode1
LightDepth: A Resource Efficient Depth Estimation Approach for Dealing with Ground Truth Sparsity via Curriculum LearningCode1
Reconfigurable Intelligent Surface Aided Wireless Sensing for Scene 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