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 1551–1600 of 2454 papers

TitleStatusHype
On Robust Cross-View Consistency in Self-Supervised Monocular Depth EstimationCode0
A Novel Unsupervised Domain Adaption Method for Depth-Guided Semantic Segmentation Using Coarse-to-Fine Alignment—0
StructNeRF: Neural Radiance Fields for Indoor Scenes with Structural Hints—0
A comprehensive survey on recent deep learning-based methods applied to surgical data—0
CLONeR: Camera-Lidar Fusion for Occupancy Grid-aided Neural Representations—0
Multi-View Reconstruction using Signed Ray Distance Functions (SRDF)—0
Rethinking Skip Connections in Encoder-decoder Networks for Monocular Depth Estimation—0
SphereDepth: Panorama Depth Estimation from Spherical Domain—0
A Practical Calibration Method for RGB Micro-Grid Polarimetric CamerasCode0
Neural Camera Models—0
Uncertainty Guided Depth Fusion for Spike Camera—0
Unsupervised Spike Depth Estimation via Cross-modality Cross-domain Knowledge TransferCode0
Dense Depth Distillation with Out-of-Distribution Simulated Images—0
A Compacted Structure for Cross-domain learning on Monocular Depth and Flow Estimation—0
DepthFake: a depth-based strategy for detecting Deepfake videos—0
Towards an AI-based Early Warning System for Bridge Scour—0
Minimizing the Effect of Noise and Limited Dataset Size in Image Classification Using Depth Estimation as an Auxiliary Task with Deep Multitask Learning—0
STS: Surround-view Temporal Stereo for Multi-view 3D Detection—0
Multi-task Learning for Monocular Depth and Defocus Estimations with Real ImagesCode0
MonoSIM: Simulating Learning Behaviors of Heterogeneous Point Cloud Object Detectors for Monocular 3D Object DetectionCode0
Rain Removal from Light Field Images with 4D Convolution and Multi-scale Gaussian ProcessCode0
An Adversarial Generative Network Designed for High-Resolution Monocular Depth Estimation from 2D HiRISE Images of MarsCode0
Improved Multiple-Image-Based Reflection Removal Algorithm Using Deep Neural Networks—0
Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions—0
Learning Feature Decomposition for Domain Adaptive Monocular Depth Estimation—0
Seeing Far in the Dark with Patterned FlashCode0
Focal-WNet: An Architecture Unifying Convolution and Attention for Depth EstimationCode0
SelectionConv: Convolutional Neural Networks for Non-rectilinear Image DataCode0
MonoIndoor++:Towards Better Practice of Self-Supervised Monocular Depth Estimation for Indoor Environments—0
DiffuStereo: High Quality Human Reconstruction via Diffusion-based Stereo Using Sparse Cameras—0
Mutual Adaptive Reasoning for Monocular 3D Multi-Person Pose Estimation—0
Adversarial Attacks on Monocular Pose EstimationCode0
Robust and accurate depth estimation by fusing LiDAR and Stereo—0
Joint Prediction of Monocular Depth and Structure using Planar and Parallax Geometry—0
Hybrid Skip: A Biologically Inspired Skip Connection for the UNet Architecture—0
Depth Perspective-aware Multiple Object Tracking—0
Direct Handheld Burst Imaging to Simulated Defocus—0
BlindSpotNet: Seeing Where We Cannot See—0
False Negative Reduction in Semantic Segmentation under Domain Shift using Depth EstimationCode0
DRL-ISP: Multi-Objective Camera ISP with Deep Reinforcement Learning—0
Gaze-Vergence-Controlled See-Through Vision in Augmented Reality—0
Multiview Detection with Cardboard Human ModelingCode0
Beyond Visual Field of View: Perceiving 3D Environment with Echoes and Vision—0
PS^2F: Polarized Spiral Point Spread Function for Single-Shot 3D Sensing—0
How Far Can I Go ? : A Self-Supervised Approach for Deterministic Video Depth ForecastingCode0
Recovering Detail in 3D Shapes Using Disparity Maps—0
Accurate and Real-time Pseudo Lidar Detection: Is Stereo Neural Network Really Necessary?—0
Monocular Depth Decomposition of Semi-Transparent Volume RenderingsCode0
Monocular Spherical Depth Estimation with Explicitly Connected Weak Layout Cues—0
A High Resolution Multi-exposure Stereoscopic Image & Video Database of Natural Scenes—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