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 1451–1500 of 2454 papers

TitleStatusHype
Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report—0
Fast and Efficient Lenslet Image Compression—0
Fast camera focus estimation for gaze-based focus control—0
Fast Neural Architecture Search for Lightweight Dense Prediction Networks—0
Fast Underwater Scene Reconstruction using Multi-View Stereo and Physical Imaging—0
f-Cal: Calibrated aleatoric uncertainty estimation from neural networks for robot perception—0
fCOP: Focal Length Estimation from Category-level Object Priors—0
Feature-Level Collaboration: Joint Unsupervised Learning of Optical Flow, Stereo Depth and Camera Motion—0
Federated Self-Supervised Learning of Monocular Depth Estimators for Autonomous Vehicles—0
FEDORA: Flying Event Dataset fOr Reactive behAvior—0
FewViewGS: Gaussian Splatting with Few View Matching and Multi-stage Training—0
FG-Depth: Flow-Guided Unsupervised Monocular Depth Estimation—0
FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation—0
Fine Dense Alignment of Image Bursts through Camera Pose and Depth Estimation—0
FIReStereo: Forest InfraRed Stereo Dataset for UAS Depth Perception in Visually Degraded Environments—0
FisheyeDistill: Self-Supervised Monocular Depth Estimation with Ordinal Distillation for Fisheye Cameras—0
FIS-Nets: Full-image Supervised Networks for Monocular Depth Estimation—0
FLaME: Fast Lightweight Mesh Estimation Using Variational Smoothing on Delaunay Graphs—0
Flexible Depth Completion for Sparse and Varying Point Densities—0
Flow-Anything: Learning Real-World Optical Flow Estimation from Large-Scale Single-view Images—0
FlowDepth: Decoupling Optical Flow for Self-Supervised Monocular Depth Estimation—0
Flowing from Words to Pixels: A Framework for Cross-Modality Evolution—0
Flowing from Words to Pixels: A Noise-Free Framework for Cross-Modality Evolution—0
Flow-NeRF: Joint Learning of Geometry, Poses, and Dense Flow within Unified Neural Representations—0
Focal Depth Estimation: A Calibration-Free, Subject- and Daytime Invariant Approach—0
FocDepthFormer: Transformer with latent LSTM for Depth Estimation from Focal Stack—0
Forest Inspection Dataset for Aerial Semantic Segmentation and Depth Estimation—0
Foundation Models Meet Low-Cost Sensors: Test-Time Adaptation for Rescaling Disparity for Zero-Shot Metric Depth Estimation—0
FoVA-Depth: Field-of-View Agnostic Depth Estimation for Cross-Dataset Generalization—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
Free Supervision From Video Games—0
FreSca: Unveiling the Scaling Space in Diffusion Models—0
From 2D to 3D: Re-thinking Benchmarking of Monocular Depth Prediction—0
From-Ground-To-Objects: Coarse-to-Fine Self-supervised Monocular Depth Estimation of Dynamic Objects with Ground Contact Prior—0
From Image to Video: An Empirical Study of Diffusion Representations—0
From Pixels to Damage Severity: Estimating Earthquake Impacts Using Semantic Segmentation of Social Media Images—0
From Real to Synthetic and Back: Synthesizing Training Data for Multi-Person Scene Understanding—0
From Single Images to Motion Policies via Video-Generation Environment Representations—0
FrozenRecon: Pose-free 3D Scene Reconstruction with Frozen Depth Models—0
FS-Depth: Focal-and-Scale Depth Estimation from a Single Image in Unseen Indoor Scene—0
FSNet: Redesign Self-Supervised MonoDepth for Full-Scale Depth Prediction for Autonomous Driving—0
Fully Convolutional Networks for Monocular Retinal Depth Estimation and Optic Disc-Cup Segmentation—0
Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing—0
Fully Hyperbolic Convolutional Neural Networks—0
Towards Long-Range 3D Object Detection for Autonomous Vehicles—0
FusionDepth: Complement Self-Supervised Monocular Depth Estimation with Cost Volume—0
FusionFormer: A Multi-sensory Fusion in Bird's-Eye-View and Temporal Consistent Transformer for 3D Object Detection—0
FusionMapping: Learning Depth Prediction with Monocular Images and 2D Laser Scans—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