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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1501–1550 of 2454 papers

TitleStatusHype
GARF:Geometry-Aware Generalized Neural Radiance Field—0
3D Object Aided Self-Supervised Monocular Depth Estimation—0
Geometry-Aware Network for Domain Adaptive Semantic Segmentation—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
Weakly Supervised 3D Multi-person Pose Estimation for Large-scale Scenes based on Monocular Camera and Single LiDAR—0
Attention-Based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object DetectionCode0
Generalized Face Anti-Spoofing via Multi-Task Learning and One-Side Meta Triplet Loss—0
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
MSS-DepthNet: Depth Prediction with Multi-Step Spiking Neural Network—0
Dynamic Depth-Supervised NeRF for Multi-View RGB-D Operating Room Images—0
Event Transformer+. A multi-purpose solution for efficient event data processing—0
Hybrid Transformer Based Feature Fusion for Self-Supervised Monocular Depth Estimation—0
DesNet: Decomposed Scale-Consistent Network for Unsupervised Depth Completion—0
Improving Pixel-Level Contrastive Learning by Leveraging Exogenous Depth Information—0
Reconfigurable Intelligent Surface Aided Wireless Sensing for Scene Depth EstimationCode0
3D-SiamMask: Vision-Based Multi-Rotor Aerial-Vehicle Tracking for a Moving ObjectCode0
Learning Phase Mask for Privacy-Preserving Passive Depth Estimation—0
Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report—0
Multi-Camera Calibration Free BEV Representation for 3D Object Detection—0
Boosting Monocular 3D Object Detection with Object-Centric Auxiliary Depth Supervision—0
Matching entropy based disparity estimation from light field—0
Robust Monocular Localization of Drones by Adapting Domain Maps to Depth Prediction Inaccuracies—0
2T-UNET: A Two-Tower UNet with Depth Clues for Robust Stereo Depth Estimation—0
Towards Comprehensive Representation Enhancement in Semantics-guided Self-supervised Monocular Depth Estimation—0
Photo-realistic Neural Domain Randomization—0
High-Resolution Depth Estimation for 360-degree Panoramas through Perspective and Panoramic Depth Images Registration—0
Hierarchical Normalization for Robust Monocular Depth Estimation—0
MonoDVPS: A Self-Supervised Monocular Depth Estimation Approach to Depth-aware Video Panoptic Segmentation—0
Multi-Task Meta Learning: learn how to adapt to unseen tasksCode0
Composite Learning for Robust and Effective Dense Predictions—0
Improving the Reliability for Confidence Estimation—0
U-HRNet: Delving into Improving Semantic Representation of High Resolution Network for Dense PredictionCode0
MonoNeRF: Learning Generalizable NeRFs from Monocular Videos without Camera Pose—0
TRADE: Object Tracking with 3D Trajectory and Ground Depth Estimates for UAVs—0
Self-Supervised Monocular Depth Underwater—0
MOTSLAM: MOT-assisted monocular dynamic SLAM using single-view depth estimation—0
Multi-Camera Collaborative Depth Prediction via Consistent Structure Estimation—0
Depth Is All You Need for Monocular 3D Detection—0
Non-learning Stereo-aided Depth Completion under Mis-projection via Selective Stereo Matching—0
Contrastive Unsupervised Learning of World Model with Invariant Causal Features—0
Lightweight Monocular Depth Estimation with an Edge Guided Network—0
Scalable and Equivariant Spherical CNNs by Discrete-Continuous (DISCO) Convolutions—0
WaterNeRF: Neural Radiance Fields for Underwater Scenes—0
Towards Multimodal Multitask Scene Understanding Models for Indoor Mobile Agents—0
DELTAR: Depth Estimation from a Light-weight ToF Sensor and RGB Image—0
DeepFusion: A Robust and Modular 3D Object Detector for Lidars, Cameras and Radars—0
Image-to-Image Translation for Autonomous Driving from Coarsely-Aligned Image Pairs—0
Self-supervised 3D Object Detection from Monocular Pseudo-LiDAR—0
Show:102550
← PrevPage 31 of 50Next →
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