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 651–700 of 2454 papers

TitleStatusHype
Forest Inspection Dataset for Aerial Semantic Segmentation and Depth Estimation—0
Confidence-Aware RGB-D Face Recognition via Virtual Depth Synthesis—0
What Matters When Repurposing Diffusion Models for General Dense Perception Tasks?Code3
On depth prediction for autonomous driving using self-supervised learning—0
DO3D: Self-supervised Learning of Decomposed Object-aware 3D Motion and Depth from Monocular Videos—0
Stealing Stable Diffusion Prior for Robust Monocular Depth EstimationCode1
OccFusion: Depth Estimation Free Multi-sensor Fusion for 3D Occupancy Prediction—0
Density-Regression: Efficient and Distance-Aware Deep Regressor for Uncertainty Estimation under Distribution ShiftsCode0
Depth-aware Test-Time Training for Zero-shot Video Object SegmentationCode1
Multi-task Learning for Real-time Autonomous Driving Leveraging Task-adaptive Attention Generator—0
Scene Depth Estimation from Traditional Oriental Landscape Paintings—0
Iterative Occlusion-Aware Light Field Depth Estimation using 4D Geometrical Cues—0
DD-VNB: A Depth-based Dual-Loop Framework for Real-time Visually Navigated Bronchoscopy—0
Scalable Vision-Based 3D Object Detection and Monocular Depth Estimation for Autonomous DrivingCode1
Kick Back & Relax++: Scaling Beyond Ground-Truth Depth with SlowTV & CribsTVCode2
Pyramid Feature Attention Network for Monocular Depth Prediction—0
Depth Estimation Algorithm Based on Transformer-Encoder and Feature Fusion—0
Beyond Inference: Performance Analysis of DNN Server Overheads for Computer Vision—0
Boosting Box-supervised Instance Segmentation with Pseudo Depth—0
Depth Information Assisted Collaborative Mutual Promotion Network for Single Image DehazingCode2
PCDepth: Pattern-based Complementary Learning for Monocular Depth Estimation by Best of Both Worlds—0
Self-Supervised Spatially Variant PSF Estimation for Aberration-Aware Depth-from-Defocus—0
CFDNet: A Generalizable Foggy Stereo Matching Network with Contrastive Feature Distillation—0
Passive Snapshot Coded Aperture Dual-Pixel RGB-D Imaging—0
A Vanilla Multi-Task Framework for Dense Visual Prediction Solution to 1st VCL Challenge -- Multi-Task Robustness Track—0
Automated Floodwater Depth Estimation Using Large Multimodal Model for Rapid Flood Mapping—0
GAM-Depth: Self-Supervised Indoor Depth Estimation Leveraging a Gradient-Aware Mask and Semantic ConstraintsCode1
TIE-KD: Teacher-Independent and Explainable Knowledge Distillation for Monocular Depth EstimationCode0
NeRF-Det++: Incorporating Semantic Cues and Perspective-aware Depth Supervision for Indoor Multi-View 3D DetectionCode1
Zero-BEV: Zero-shot Projection of Any First-Person Modality to BEV Maps—0
Unveiling the Depths: A Multi-Modal Fusion Framework for Challenging Scenarios—0
SDGE: Stereo Guided Depth Estimation for 360^ Camera Sets—0
An Endoscopic Chisel: Intraoperative Imaging Carves 3D Anatomical Models—0
MAL: Motion-Aware Loss with Temporal and Distillation Hints for Self-Supervised Depth Estimation—0
Efficient Multi-task Uncertainties for Joint Semantic Segmentation and Monocular Depth Estimation—0
X-maps: Direct Depth Lookup for Event-based Structured Light SystemsCode2
Depth-aware Volume Attention for Texture-less Stereo MatchingCode1
Learn to Teach: Sample-Efficient Privileged Learning for Humanoid Locomotion over Diverse Terrains—0
Hybridnet for depth estimation and semantic segmentation—0
Adaptive Surface Normal Constraint for Geometric Estimation from Monocular Images—0
Toward Accurate Camera-based 3D Object Detection via Cascade Depth Estimation and Calibration—0
Energy-based Domain-Adaptive Segmentation with Depth Guidance—0
MoD-SLAM: Monocular Dense Mapping for Unbounded 3D Scene Reconstruction—0
An Inpainting-Infused Pipeline for Attire and Background Replacement—0
CLIP Can Understand Depth—0
Decomposition-based and Interference Perception for Infrared and Visible Image Fusion in Complex Scenes—0
RIDERS: Radar-Infrared Depth Estimation for Robust SensingCode1
Convolution kernel adaptation to calibrated fisheyeCode0
Diffusion-based Light Field Synthesis—0
Depth Anything in Medical Images: A Comparative Study—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