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 1151–1200 of 2454 papers

TitleStatusHype
The Third Monocular Depth Estimation Challenge—0
MonoPCC: Photometric-invariant Cycle Constraint for Monocular Depth Estimation of Endoscopic Images—0
SGFormer: Spherical Geometry Transformer for 360 Depth Estimation—0
Self-Supervised Monocular Depth Estimation in the Dark: Towards Data Distribution Compensation—0
GScream: Learning 3D Geometry and Feature Consistent Gaussian Splatting for Object Removal—0
High-fidelity Endoscopic Image Synthesis by Utilizing Depth-guided Neural Surfaces—0
BLINK: Multimodal Large Language Models Can See but Not Perceive—0
How to deal with glare for improved perception of Autonomous Vehicles—0
Virtually Enriched NYU Depth V2 Dataset for Monocular Depth Estimation: Do We Need Artificial Augmentation?Code0
On the Robustness of Language Guidance for Low-Level Vision Tasks: Findings from Depth EstimationCode0
Into the Fog: Evaluating Robustness of Multiple Object TrackingCode0
GLID: Pre-training a Generalist Encoder-Decoder Vision Model—0
Depth Estimation using Weighted-loss and Transfer Learning—0
Implicit and Explicit Language Guidance for Diffusion-based Visual Perception—0
Boosting Self-Supervision for Single-View Scene Completion via Knowledge Distillation—0
Self-supervised Monocular Depth Estimation on Water Scenes via Specular Reflection Prior—0
MonoSelfRecon: Purely Self-Supervised Explicit Generalizable 3D Reconstruction of Indoor Scenes from Monocular RGB Views—0
Enhanced Radar Perception via Multi-Task Learning: Towards Refined Data for Sensor Fusion Applications—0
Incremental Joint Learning of Depth, Pose and Implicit Scene Representation on Monocular Camera in Large-scale Scenes—0
HawkDrive: A Transformer-driven Visual Perception System for Autonomous Driving in Night SceneCode0
Deep Phase Coded Image Prior—0
MVD-Fusion: Single-view 3D via Depth-consistent Multi-view Generation—0
Adaptive Discrete Disparity Volume for Self-supervised Monocular Depth Estimation—0
CHOSEN: Contrastive Hypothesis Selection for Multi-View Depth Refinement—0
Improving Bird's Eye View Semantic Segmentation by Task Decomposition—0
MaGRITTe: Manipulative and Generative 3D Realization from Image, Topview and Text—0
NeSLAM: Neural Implicit Mapping and Self-Supervised Feature Tracking With Depth Completion and DenoisingCode0
FlowDepth: Decoupling Optical Flow for Self-Supervised Monocular Depth Estimation—0
SAID-NeRF: Segmentation-AIDed NeRF for Depth Completion of Transparent Objects—0
F^2Depth: Self-supervised Indoor Monocular Depth Estimation via Optical Flow Consistency and Feature Map Synthesis—0
Track Everything Everywhere Fast and Robustly—0
Leveraging Near-Field Lighting for Monocular Depth Estimation from Endoscopy Videos—0
Elite360D: Towards Efficient 360 Depth Estimation via Semantic- and Distance-Aware Bi-Projection Fusion—0
Spike-NeRF: Neural Radiance Field Based On Spike Camera—0
Configurable Holography: Towards Display and Scene Adaptation—0
Depth Estimation fusing Image and Radar Measurements with Uncertain Directions—0
Language-Based Depth Hints for Monocular Depth Estimation—0
Learning to Project for Cross-Task Knowledge Distillation—0
Geometric Constraints in Deep Learning Frameworks: A Survey—0
FutureDepth: Learning to Predict the Future Improves Video Depth Estimation—0
GraphBEV: Towards Robust BEV Feature Alignment for Multi-Modal 3D Object Detection—0
SSAP: A Shape-Sensitive Adversarial Patch for Comprehensive Disruption of Monocular Depth Estimation in Autonomous Navigation Applications—0
Region-aware Distribution Contrast: A Novel Approach to Multi-Task Partially Supervised Learning—0
Touch-GS: Visual-Tactile Supervised 3D Gaussian Splatting—0
Improving Distant 3D Object Detection Using 2D Box Supervision—0
METER: a mobile vision transformer architecture for monocular depth estimationCode0
Q-SLAM: Quadric Representations for Monocular SLAM—0
D4D: An RGBD diffusion model to boost monocular depth estimationCode0
WaveShot: A Compact Portable Unmanned Surface Vessel for Dynamic Water Surface Videography and Media Production—0
SGE: Structured Light System Based on Gray Code with an Event Camera—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