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 801–850 of 2454 papers

TitleStatusHype
ConsistNet: Enforcing 3D Consistency for Multi-view Images DiffusionCode1
PU-Ray: Domain-Independent Point Cloud Upsampling via Ray Marching on Neural Implicit SurfaceCode0
EC-Depth: Exploring the consistency of self-supervised monocular depth estimation in challenging scenesCode1
Multi-Task Learning-Enabled Automatic Vessel Draft Reading for Intelligent Maritime Surveillance—0
JointNet: Extending Text-to-Image Diffusion for Dense Distribution Modeling—0
WeatherDepth: Curriculum Contrastive Learning for Self-Supervised Depth Estimation under Adverse Weather ConditionsCode1
Towards Long-Range 3D Object Detection for Autonomous Vehicles—0
Federated Self-Supervised Learning of Monocular Depth Estimators for Autonomous Vehicles—0
MeSa: Masked, Geometric, and Supervised Pre-training for Monocular Depth Estimation—0
Sub-token ViT Embedding via Stochastic Resonance TransformersCode0
RSRD: A Road Surface Reconstruction Dataset and Benchmark for Safe and Comfortable Autonomous Driving—0
Selective Feature Adapter for Dense Vision Transformers—0
Skin the sheep not only once: Reusing Various Depth Datasets to Drive the Learning of Optical Flow—0
Multi-task Learning with 3D-Aware RegularizationCode1
Task-guided Domain Gap Reduction for Monocular Depth Prediction in EndoscopyCode0
Win-Win: Training High-Resolution Vision Transformers from Two Windows—0
InstructCV: Instruction-Tuned Text-to-Image Diffusion Models as Vision GeneralistsCode2
Text-image Alignment for Diffusion-based PerceptionCode1
IFAST: Weakly Supervised Interpretable Face Anti-spoofing from Single-shot Binocular NIR Images—0
GSDC Transformer: An Efficient and Effective Cue Fusion for Monocular Multi-Frame Depth Estimation—0
Gated Cross-Attention Network for Depth Completion—0
Finite Scalar Quantization: VQ-VAE Made SimpleCode1
GasMono: Geometry-Aided Self-Supervised Monocular Depth Estimation for Indoor ScenesCode1
M^33D: Learning 3D priors using Multi-Modal Masked Autoencoders for 2D image and video understanding—0
ADU-Depth: Attention-based Distillation with Uncertainty Modeling for Depth Estimation—0
IEBins: Iterative Elastic Bins for Monocular Depth EstimationCode1
DISeR: Designing Imaging Systems with Reinforcement Learning—0
InSpaceType: Reconsider Space Type in Indoor Monocular Depth EstimationCode1
SRFNet: Monocular Depth Estimation with Fine-grained Structure via Spatial Reliability-oriented Fusion of Frames and Events—0
SANPO: A Scene Understanding, Accessibility and Human Navigation Dataset—0
Dense 2D-3D Indoor Prediction with Sound via Aligned Cross-Modal DistillationCode0
BroadBEV: Collaborative LiDAR-camera Fusion for Broad-sighted Bird's Eye View Map Construction—0
NDDepth: Normal-Distance Assisted Monocular Depth EstimationCode1
Large-scale Monocular Depth Estimation in the Wild—0
GEDepth: Ground Embedding for Monocular Depth EstimationCode1
Robust Geometry-Preserving Depth Estimation Using Differentiable Rendering—0
Deep Neighbor Layer Aggregation for Lightweight Self-Supervised Monocular Depth EstimationCode0
DEUX: Active Exploration for Learning Unsupervised Depth Perception—0
X-PDNet: Accurate Joint Plane Instance Segmentation and Monocular Depth Estimation with Cross-Task Distillation and Boundary CorrectionCode1
Depth Estimation from a Single Optical Encoded Image using a Learned Colored-Coded Aperture—0
AmodalSynthDrive: A Synthetic Amodal Perception Dataset for Autonomous Driving—0
Towards Better Data Exploitation in Self-Supervised Monocular Depth EstimationCode1
FusionFormer: A Multi-sensory Fusion in Bird's-Eye-View and Temporal Consistent Transformer for 3D Object Detection—0
Robot Localization and Mapping Final Report -- Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry—0
Joint Self-supervised Depth and Optical Flow Estimation towards Dynamic Objects—0
Bayes' Rays: Uncertainty Quantification for Neural Radiance FieldsCode1
Two-in-One Depth: Bridging the Gap Between Monocular and Binocular Self-supervised Depth EstimationCode1
RigNet++: Semantic Assisted Repetitive Image Guided Network for Depth Completion—0
SQLdepth: Generalizable Self-Supervised Fine-Structured Monocular Depth EstimationCode1
Structure-Aware Parametric Representations for Time-Resolved Light Transport—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