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 2151–2200 of 2454 papers

TitleStatusHype
PLADE-Net: Towards Pixel-Level Accuracy for Self-Supervised Single-View Depth Estimation with Neural Positional Encoding and Distilled Matting LossCode0
Pixel-Accurate Depth Evaluation in Realistic Driving ScenariosCode0
A step towards understanding why classification helps regressionCode0
PID4LaTe: a physics-informed deep learning model for lake multi-depth temperature predictionCode0
PhaseCam3D — Learning Phase Masks for Passive Single View Depth EstimationCode0
DepthFormer: Exploiting Long-Range Correlation and Local Information for Accurate Monocular Depth EstimationCode0
Visual Domain Adaptation for Monocular Depth Estimation on Resource-Constrained HardwareCode0
Challenges of Multi-Modal Coreset Selection for Depth PredictionCode0
SpaRC: Sparse Radar-Camera Fusion for 3D Object DetectionCode0
Parse Geometry from a Line: Monocular Depth Estimation with Partial Laser ObservationCode0
Geo-Supervised Visual Depth PredictionCode0
RadarRGBD A Multi-Sensor Fusion Dataset for Perception with RGB-D and mmWave RadarCode0
Panoramic Depth Estimation via Supervised and Unsupervised Learning in Indoor ScenesCode0
Rain Removal from Light Field Images with 4D Convolution and Multi-scale Gaussian ProcessCode0
Depth Estimation via Affinity Learned with Convolutional Spatial Propagation NetworkCode0
P3P: Pseudo-3D Pre-training for Scaling 3D Voxel-based Masked AutoencodersCode0
Sparse and noisy LiDAR completion with RGB guidance and uncertaintyCode0
Sparse and noisy LiDAR completion with RGB guidance anduncertaintyCode0
Geometry meets semantics for semi-supervised monocular depth estimationCode0
On the Robustness of Language Guidance for Low-Level Vision Tasks: Findings from Depth EstimationCode0
Geometry-Aware Symmetric Domain Adaptation for Monocular Depth EstimationCode0
Nighttime Stereo Depth Estimation using Joint Translation-Stereo Learning: Light Effects and Uninformative RegionsCode0
Sparse Depth Sensing for Resource-Constrained RobotsCode0
RealNet: Combining Optimized Object Detection with Information Fusion Depth Estimation Co-Design Method on IoTCode0
Real Time Dense Depth Estimation by Fusing Stereo with Sparse Depth MeasurementsCode0
Camera-Independent Single Image Depth Estimation from Defocus BlurCode0
Sparse-to-Continuous: Enhancing Monocular Depth Estimation using Occupancy MapsCode0
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single ImageCode0
Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric AnnotationsCode0
On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network ApproachCode0
Generating and Exploiting Probabilistic Monocular Depth EstimatesCode0
On the Benefit of Adversarial Training for Monocular Depth EstimationCode0
Sparsity Invariant CNNsCode0
On Robust Cross-View Consistency in Self-Supervised Monocular Depth EstimationCode0
CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View DepthCode0
Reconfigurable Intelligent Surface Aided Wireless Sensing for Scene Depth EstimationCode0
CAD2RL: Real Single-Image Flight without a Single Real ImageCode0
FUSE: Label-Free Image-Event Joint Monocular Depth Estimation via Frequency-Decoupled Alignment and Degradation-Robust FusionCode0
WoodScape: A multi-task, multi-camera fisheye dataset for autonomous drivingCode0
Recurrent Neural Network for (Un-)supervised Learning of Monocular VideoVisual Odometry and DepthCode0
Enhanced Encoder-Decoder Architecture for Accurate Monocular Depth EstimationCode0
Recurrent Scene Parsing with Perspective Understanding in the LoopCode0
Density-Regression: Efficient and Distance-Aware Deep Regressor for Uncertainty Estimation under Distribution ShiftsCode0
Dense Scene Reconstruction from Light-Field Images Affected by Rolling ShutterCode0
Visualization of Convolutional Neural Networks for Monocular Depth EstimationCode0
OmniMVS: End-to-End Learning for Omnidirectional Stereo MatchingCode0
An Adversarial Generative Network Designed for High-Resolution Monocular Depth Estimation from 2D HiRISE Images of MarsCode0
Dense 2D-3D Indoor Prediction with Sound via Aligned Cross-Modal DistillationCode0
Region Deformer Networks for Unsupervised Depth Estimation from Unconstrained Monocular VideosCode0
OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous DrivingCode0
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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