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
Towards Explainability in Monocular Depth Estimation—0
Towards Full-parameter and Parameter-efficient Self-learning For Endoscopic Camera Depth Estimation—0
Towards Full-to-Empty Room Generation with Structure-Aware Feature Encoding and Soft Semantic Region-Adaptive Normalization—0
Towards General Purpose Geometry-Preserving Single-View Depth Estimation—0
Towards Holistic Scene Understanding: Feedback Enabled Cascaded Classification Models—0
Towards Keypoint Guided Self-Supervised Depth Estimation—0
Towards Multimodal Depth Estimation from Light Fields—0
Towards Multimodal Multitask Scene Understanding Models for Indoor Mobile Agents—0
Towards Panoptic 3D Parsing for Single Image in the Wild—0
Towards Robust Monocular Depth Estimation in Non-Lambertian Surfaces—0
Towards Scale-Aware Full Surround Monodepth with Transformers—0
Towards Scene Understanding: Unsupervised Monocular Depth Estimation With Semantic-Aware Representation—0
Towards Unified Depth and Semantic Prediction From a Single Image—0
Towards urban scenes understanding through polarization cues—0
Track Everything Everywhere Fast and Robustly—0
Tracktention: Leveraging Point Tracking to Attend Videos Faster and Better—0
TRADE: Object Tracking with 3D Trajectory and Ground Depth Estimates for UAVs—0
Auxiliary Learning for Deep Multi-task Learning—0
Transparent Object Depth Completion—0
TranSplat: Generalizable 3D Gaussian Splatting from Sparse Multi-View Images with Transformers—0
TransPose: A Transformer-based 6D Object Pose Estimation Network with Depth Refinement—0
Trust Your Model: Light Field Depth Estimation With Inline Occlusion Handling—0
TSGS: Improving Gaussian Splatting for Transparent Surface Reconstruction via Normal and De-lighting Priors—0
T-SVG: Text-Driven Stereoscopic Video Generation—0
TULIP: Towards Unified Language-Image Pretraining—0
TW-SMNet: Deep Multitask Learning of Tele-Wide Stereo Matching—0
UAMD-Net: A Unified Adaptive Multimodal Neural Network for Dense Depth Completion—0
UASOL, a large-scale high-resolution outdoor stereo dataset—0
UC-NeRF: Neural Radiance Field for Under-Calibrated Multi-view Cameras in Autonomous Driving—0
UDGS-SLAM : UniDepth Assisted Gaussian Splatting for Monocular SLAM—0
Ultrafast High-Flux Single-Photon LiDAR Simulator via Neural Mapping—0
UMono: Physical Model Informed Hybrid CNN-Transformer Framework for Underwater Monocular Depth Estimation—0
Uncertainty and Self-Supervision in Single-View Depth—0
Uncertainty depth estimation with gated images for 3D reconstruction—0
Uncertainty-Driven Dense Two-View Structure from Motion—0
Uncertainty Guided Depth Fusion for Spike Camera—0
Uncertainty-guided Optimal Transport in Depth Supervised Sparse-View 3D Gaussian—0
UnCLe: Unsupervised Continual Learning of Depth Completion—0
UnDEMoN 2.0: Improved Depth and Ego Motion Estimation through Deep Image Sampling—0
Understanding Depth Map Progressively: Adaptive Distance Interval Separation for Monocular 3d Object Detection—0
Light Field-Based Underwater 3D Reconstruction Via Angular Resampling—0
Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning—0
UniBEVFusion: Unified Radar-Vision BEVFusion for 3D Object Detection—0
Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks—0
EMatch: A Unified Framework for Event-based Optical Flow and Stereo Matching—0
UniTT-Stereo: Unified Training of Transformer for Enhanced Stereo Matching—0
UniUIR: Considering Underwater Image Restoration as An All-in-One Learner—0
UniVG: A Generalist Diffusion Model for Unified Image Generation and Editing—0
Unpaired Single-Image Depth Synthesis with cycle-consistent Wasserstein GANs—0
UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a Generic Framework for Handling Common Camera Distortion Models—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