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 151–200 of 2454 papers

TitleStatusHype
A Light and Tuning-free Method for Simulating Camera Motion in Video GenerationCode1
Towards Ambiguity-Free Spatial Foundation Model: Rethinking and Decoupling Depth AmbiguityCode0
TomatoScanner: phenotyping tomato fruit based on only RGB imageCode0
EvidMTL: Evidential Multi-Task Learning for Uncertainty-Aware Semantic Surface Mapping from Monocular RGB Images—0
RGB-Thermal Infrared Fusion for Robust Depth Estimation in Complex Environments—0
Task-Agnostic Attacks Against Vision Foundation ModelsCode0
Multi-View Depth Consistent Image Generation Using Generative AI Models: Application on Architectural Design of University Buildings—0
MUSt3R: Multi-view Network for Stereo 3D ReconstructionCode3
Bridging Spectral-wise and Multi-spectral Depth Estimation via Geometry-guided Contrastive LearningCode1
UniDepthV2: Universal Monocular Metric Depth Estimation Made SimplerCode5
SLAM in the Dark: Self-Supervised Learning of Pose, Depth and Loop-Closure from Thermal Images—0
Distill Any Depth: Distillation Creates a Stronger Monocular Depth EstimatorCode4
RGB-Only Gaussian Splatting SLAM for Unbounded Outdoor Scenes—0
Challenges of Multi-Modal Coreset Selection for Depth PredictionCode0
OrchardDepth: Precise Metric Depth Estimation of Orchard Scene from Monocular Camera Images—0
Self-supervised Monocular Depth Estimation Robust to Reflective Surface Leveraged by Triplet Mining—0
Monocular Depth Estimation and Segmentation for Transparent Object with Iterative Semantic and Geometric FusionCode1
LXLv2: Enhanced LiDAR Excluded Lean 3D Object Detection with Fusion of 4D Radar and Camera—0
CDGS: Confidence-Aware Depth Regularization for 3D Gaussian SplattingCode1
SHADeS: Self-supervised Monocular Depth Estimation Through Non-Lambertian Image DecompositionCode0
Pre-training Auto-regressive Robotic Models with 4D Representations—0
Deep Neural Networks for Accurate Depth Estimation with Latent Space Features—0
pySLAM: An Open-Source, Modular, and Extensible Framework for SLAMCode7
Adjust Your Focus: Defocus Deblurring From Dual-Pixel Images Using Explicit Multi-Scale Cross-Correlation—0
RealCam-I2V: Real-World Image-to-Video Generation with Interactive Complex Camera Control—0
CoL3D: Collaborative Learning of Single-view Depth and Camera Intrinsics for Metric 3D Shape Recovery—0
S^2-Diffusion: Generalizing from Instance-level to Category-level Skills in Robot Manipulation—0
SteROI-D: System Design and Mapping for Stereo Depth Inference on Regions of Interest—0
Matrix3D: Large Photogrammetry Model All-in-One—0
Learning Inverse Laplacian Pyramid for Progressive Depth Completion—0
From Image to Video: An Empirical Study of Diffusion Representations—0
Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing—0
SphereFusion: Efficient Panorama Depth Estimation via Gated Fusion—0
Building Rome with Convex Optimization—0
MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images—0
DOC-Depth: A novel approach for dense depth ground truth generation—0
Leveraging Stable Diffusion for Monocular Depth Estimation via Image Semantic Encoding—0
Zero-Shot Novel View and Depth Synthesis with Multi-View Geometric Diffusion—0
Snapshot Compressed Imaging Based Single-Measurement Computer Vision for Videos—0
Rethinking Encoder-Decoder Flow Through Shared Structures—0
PromptMono: Cross Prompting Attention for Self-Supervised Monocular Depth Estimation in Challenging Environments—0
IMAGINE-E: Image Generation Intelligence Evaluation of State-of-the-art Text-to-Image ModelsCode0
Enhancing Monocular Depth Estimation with Multi-Source Auxiliary TasksCode0
UniUIR: Considering Underwater Image Restoration as An All-in-One Learner—0
Fast Underwater Scene Reconstruction using Multi-View Stereo and Physical Imaging—0
Survey on Monocular Metric Depth Estimation—0
Video Depth Anything: Consistent Depth Estimation for Super-Long VideosCode5
RDG-GS: Relative Depth Guidance with Gaussian Splatting for Real-time Sparse-View 3D Rendering—0
FoundationStereo: Zero-Shot Stereo MatchingCode7
One-D-Piece: Image Tokenizer Meets Quality-Controllable Compression—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