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 2051–2100 of 2454 papers

TitleStatusHype
RayMVSNet++: Learning Ray-based 1D Implicit Fields for Accurate Multi-View Stereo—0
RDG-GS: Relative Depth Guidance with Gaussian Splatting for Real-time Sparse-View 3D Rendering—0
RealCam-I2V: Real-World Image-to-Video Generation with Interactive Complex Camera Control—0
RealMonoDepth: Self-Supervised Monocular Depth Estimation for General Scenes—0
Real-time Dynamic Object Detection for Autonomous Driving using Prior 3D-Maps—0
Real-Time Hybrid Mapping of Populated Indoor Scenes using a Low-Cost Monocular UAV—0
Real-time Joint Object Detection and Semantic Segmentation Network for Automated Driving—0
Real-time Monocular Depth Estimation with Sparse Supervision on Mobile—0
Real-time Multi-view Omnidirectional Depth Estimation System for Robots and Autonomous Driving on Real Scenes—0
Real-time Surgical Environment Enhancement for Robot-Assisted Minimally Invasive Surgery Based on Super-Resolution—0
ReCoRe: Regularized Contrastive Representation Learning of World Model—0
Rectification-specific Supervision and Constrained Estimator for Online Stereo Rectification—0
Recurrent Neural Network for Learning DenseDepth and Ego-Motion from Video—0
Recurrent Neural Network for (Un-)Supervised Learning of Monocular Video Visual Odometry and Depth—0
Recurrent Structure Attention Guidance for Depth Super-Resolution—0
RefGaussian: Disentangling Reflections from 3D Gaussian Splatting for Realistic Rendering—0
Refine and Distill: Exploiting Cycle-Inconsistency and Knowledge Distillation for Unsupervised Monocular Depth Estimation—0
Reflection Separation and Deblurring of Plenoptic Images—0
Region-aware Distribution Contrast: A Novel Approach to Multi-Task Partially Supervised Learning—0
Reinforcement Learning-Based Monocular Vision Approach for Autonomous UAV Landing—0
Relative Depth Estimation as a Ranking Problem—0
ResearchDoom and CocoDoom: Learning Computer Vision with Games—0
Residual-Guided Learning Representation for Self-Supervised Monocular Depth Estimation—0
Rethinking Encoder-Decoder Flow Through Shared Structures—0
Rethinking Monocular Depth Estimation with Adversarial Training—0
Rethinking Skip Connections in Encoder-decoder Networks for Monocular Depth Estimation—0
Retrieving snow depth distribution by downscaling ERA5 Reanalysis with ICESat-2 laser altimetry—0
Revealing the Reciprocal Relations Between Self-Supervised Stereo and Monocular Depth Estimation—0
Review on Panoramic Imaging and Its Applications in Scene Understanding—0
Revisiting 360 Depth Estimation with PanoGabor: A New Fusion Perspective—0
Revisiting Birds Eye View Perception Models with Frozen Foundation Models: DINOv2 and Metric3Dv2—0
Revisiting Disparity from Dual-Pixel Images: Physics-Informed Lightweight Depth Estimation—0
Revisiting Monocular 3D Object Detection from Scene-Level Depth Retargeting to Instance-Level Spatial Refinement—0
Revisit Self-supervised Depth Estimation with Local Structure-from-Motion—0
RGB-Only Gaussian Splatting SLAM for Unbounded Outdoor Scenes—0
Richardson-Lucy Deblurring for Moving Light Field Cameras—0
RigNet++: Semantic Assisted Repetitive Image Guided Network for Depth Completion—0
RigNet: Repetitive Image Guided Network for Depth Completion—0
RingMoE: Mixture-of-Modality-Experts Multi-Modal Foundation Models for Universal Remote Sensing Image Interpretation—0
Robot Localization and Mapping Final Report -- Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry—0
Robust 3D reconstruction of dynamic scenes from single-photon lidar using Beta-divergences—0
Robust and accurate depth estimation by fusing LiDAR and Stereo—0
Robust and Flexible Omnidirectional Depth Estimation with Multiple 360° Cameras—0
Robust Depth Estimation from Auto Bracketed Images—0
RobuSTereo: Robust Zero-Shot Stereo Matching under Adverse Weather—0
Robust Full-FoV Depth Estimation in Tele-wide Camera System—0
Robust Geometry-Preserving Depth Estimation Using Differentiable Rendering—0
Robust Light Field Depth Estimation for Noisy Scene With Occlusion—0
Robust Monocular Depth Estimation under Challenging Conditions—0
Robust Monocular Localization of Drones by Adapting Domain Maps to Depth Prediction Inaccuracies—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