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 701–750 of 2454 papers

TitleStatusHype
Image Masking for Robust Self-Supervised Monocular Depth EstimationCode1
Learning to Upsample by Learning to SampleCode1
NVDS+: Towards Efficient and Versatile Neural Stabilizer for Video Depth EstimationCode1
SparseTrack: Multi-Object Tracking by Performing Scene Decomposition based on Pseudo-DepthCode1
RM-Depth: Unsupervised Learning of Recurrent Monocular Depth in Dynamic ScenesCode1
Edge-aware Bidirectional Diffusion for Dense Depth Estimation from Light FieldsCode1
Exploring Depth Contribution for Camouflaged Object Detection—0
Depth Completion via Inductive Fusion of Planar LIDAR and Monocular Camera—0
Boosting Monocular Depth Estimation with Lightweight 3D Point Fusion—0
Depth Completion using Plane-Residual Representation—0
Analysis and Improvement of Rank-Ordered Mean Algorithm in Single-Photon LiDAR—0
Depth-Centric Dehazing and Depth-Estimation from Real-World Hazy Driving Video—0
Depth by Poking: Learning to Estimate Depth from Self-Supervised Grasping—0
Boosting Monocular 3D Object Detection with Object-Centric Auxiliary Depth Supervision—0
Analog Signal Processing Approach for Coarse and Fine Depth Estimation—0
AcousticFusion: Fusing Sound Source Localization to Visual SLAM in Dynamic Environments—0
Depth-aware Neural Style Transfer using Instance Normalization—0
Boosting Generalizability towards Zero-Shot Cross-Dataset Single-Image Indoor Depth by Meta-Initialization—0
Depth Assisted Full Resolution Network for Single Image-based View Synthesis—0
Boosting Box-supervised Instance Segmentation with Pseudo Depth—0
An Advert Creation System for 3D Product Placements—0
DepthART: Monocular Depth Estimation as Autoregressive Refinement Task—0
Depth Anywhere: Enhancing 360 Monocular Depth Estimation via Perspective Distillation and Unlabeled Data Augmentation—0
Booster: a Benchmark for Depth from Images of Specular and Transparent Surfaces—0
Depth Anything with Any Prior—0
A Construct-Optimize Approach to Sparse View Synthesis without Camera Pose—0
2T-UNET: A Two-Tower UNet with Depth Clues for Robust Stereo Depth Estimation—0
Embodiment: Self-Supervised Depth Estimation Based on Camera Models—0
Bokeh Rendering Based on Adaptive Depth Calibration Network—0
Depth Anything in Medical Images: A Comparative Study—0
An Adaptive Framework for Missing Depth Inference Using Joint Bilateral Filter—0
Depth and Image Restoration From Light Field in a Scattering Medium—0
Depth360: Self-supervised Learning for Monocular Depth Estimation using Learnable Camera Distortion Model—0
Blurry-Edges: Photon-Limited Depth Estimation from Defocused Boundaries—0
A Multi-modal Approach to Single-modal Visual Place Classification—0
Dense Depth Estimation of a Complex Dynamic Scene without Explicit 3D Motion Estimation—0
BLINK: Multimodal Large Language Models Can See but Not Perceive—0
AmodalSynthDrive: A Synthetic Amodal Perception Dataset for Autonomous Driving—0
Dense monocular Simultaneous Localization and Mapping by direct surfel optimization—0
Dense Monocular Motion Segmentation Using Optical Flow and Pseudo Depth Map: A Zero-Shot Approach—0
BlindSpotNet: Seeing Where We Cannot See—0
3D Hierarchical Refinement and Augmentation for Unsupervised Learning of Depth and Pose from Monocular Video—0
Dense Monocular Depth Estimation in Complex Dynamic Scenes—0
BLADE: Single-view Body Mesh Learning through Accurate Depth Estimation—0
DenseLiDAR: A Real-Time Pseudo Dense Depth Guided Depth Completion Network—0
Dense Geometry Supervision for Underwater Depth Estimation—0
BLADE: Single-view Body Mesh Estimation through Accurate Depth Estimation—0
FPGA-based Acceleration of Neural Network for Image Classification using Vitis AI—0
FP-Stereo: Hardware-Efficient Stereo Vision for Embedded Applications—0
Fractal Pyramid Networks—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