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 401–450 of 2454 papers

TitleStatusHype
Panoptic-Depth Forecasting—0
Fine-Tuning Image-Conditional Diffusion Models is Easier than You ThinkCode4
GRIN: Zero-Shot Metric Depth with Pixel-Level Diffusion—0
Towards Single-Lens Controllable Depth-of-Field Imaging via Depth-Aware Point Spread FunctionsCode0
PrimeDepth: Efficient Monocular Depth Estimation with a Stable Diffusion PreimageCode2
Precision Aquaculture: An Integrated Computer Vision and IoT Approach for Optimized Tilapia FeedingCode0
FIReStereo: Forest InfraRed Stereo Dataset for UAS Depth Perception in Visually Degraded Environments—0
LED: Light Enhanced Depth Estimation at NightCode1
Depth on Demand: Streaming Dense Depth from a Low Frame Rate Active Sensor—0
Real-time Multi-view Omnidirectional Depth Estimation System for Robots and Autonomous Driving on Real Scenes—0
Advancing Depth Anything Model for Unsupervised Monocular Depth Estimation in Endoscopy—0
Constructive Universal Approximation and Finite Sample Memorization by Narrow Deep ReLU Networks—0
EDADepth: Enhanced Data Augmentation for Monocular Depth EstimationCode0
EndoOmni: Zero-Shot Cross-Dataset Depth Estimation in Endoscopy by Robust Self-Learning from Noisy LabelsCode1
TanDepth: Leveraging Global DEMs for Metric Monocular Depth Estimation in UAVs—0
Introducing a Class-Aware Metric for Monocular Depth Estimation: An Automotive PerspectiveCode0
Estimating Indoor Scene Depth Maps from Ultrasonic Echoes—0
SG-MIM: Structured Knowledge Guided Efficient Pre-training for Dense Prediction—0
Skip-and-Play: Depth-Driven Pose-Preserved Image Generation for Any Objects—0
MaDis-Stereo: Enhanced Stereo Matching via Distilled Masked Image Modeling—0
iConFormer: Dynamic Parameter-Efficient Tuning with Input-Conditioned Adaptation—0
Plane2Depth: Hierarchical Adaptive Plane Guidance for Monocular Depth EstimationCode2
UniTT-Stereo: Unified Training of Transformer for Enhanced Stereo Matching—0
GGS: Generalizable Gaussian Splatting for Lane Switching in Autonomous Driving—0
Boosting Generalizability towards Zero-Shot Cross-Dataset Single-Image Indoor Depth by Meta-Initialization—0
DepthCrafter: Generating Consistent Long Depth Sequences for Open-world VideosCode5
GET-UP: GEomeTric-aware Depth Estimation with Radar Points UPsamplingCode1
Large Language Models Can Understanding Depth from Monocular Images—0
Disparity Estimation Using a Quad-Pixel SensorCode1
UDGS-SLAM : UniDepth Assisted Gaussian Splatting for Monocular SLAM—0
Synthetic Lunar Terrain: A Multimodal Open Dataset for Training and Evaluating Neuromorphic Vision Algorithms—0
DARES: Depth Anything in Robotic Endoscopic Surgery with Self-supervised Vector-LoRA of the Foundation ModelCode1
Enhancing Underwater Imaging with 4-D Light Fields: Dataset and MethodCode0
Revisiting 360 Depth Estimation with PanoGabor: A New Fusion Perspective—0
EvLight++: Low-Light Video Enhancement with an Event Camera: A Large-Scale Real-World Dataset, Novel Method, and More—0
Depth Restoration of Hand-Held Transparent Objects for Human-to-Robot Handover—0
Adversarial Manhole: Challenging Monocular Depth Estimation and Semantic Segmentation Models with Patch AttackCode0
Pixel-Aligned Multi-View Generation with Depth Guided Decoder—0
NimbleD: Enhancing Self-supervised Monocular Depth Estimation with Pseudo-labels and Large-scale Video Pre-trainingCode0
TranSplat: Generalizable 3D Gaussian Splatting from Sparse Multi-View Images with Transformers—0
InSpaceType: Dataset and Benchmark for Reconsidering Cross-Space Type Performance in Indoor Monocular DepthCode1
CatFree3D: Category-agnostic 3D Object Detection with Diffusion—0
Sapiens: Foundation for Human Vision ModelsCode9
LiFCal: Online Light Field Camera Calibration via Bundle Adjustment—0
SHARP: Segmentation of Hands and Arms by Range using Pseudo-Depth for Enhanced Egocentric 3D Hand Pose Estimation and Action RecognitionCode1
P3P: Pseudo-3D Pre-training for Scaling 3D Voxel-based Masked AutoencodersCode0
Structure-preserving Image Translation for Depth Estimation in Colonoscopy VideoCode1
Enhanced Scale-aware Depth Estimation for Monocular Endoscopic Scenes with Geometric Modeling—0
Towards Robust Monocular Depth Estimation in Non-Lambertian Surfaces—0
Depth Any Canopy: Leveraging Depth Foundation Models for Canopy Height EstimationCode1
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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