On the public citation graph

Depth Estimation Through Sub-Pixel Laplacian Pyramid Network

https://doi.org/10.2139/ssrn.4855393
0 signed · 51 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-01219-9_14openalex-extracted
Monocular Depth Estimation with Affinity, Vertical Pooling, and Label Enhancement
inferredcites →https://doi.org/10.1007/978-3-319-46484-8_45openalex-extracted
Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue
inferredcites →https://doi.org/10.1007/978-3-642-33715-4_54openalex-extracted
Indoor Segmentation and Support Inference from RGBD Images
inferredcites →https://doi.org/10.1007/s11263-015-0816-yopenalex-extracted
ImageNet Large Scale Visual Recognition Challenge
inferredcites →https://doi.org/10.1016/b978-0-08-051581-6.50065-9openalex-extracted
The Laplacian Pyramid as a Compact Image Code
inferredcites →https://doi.org/10.1016/j.cag.2022.06.009openalex-extracted
Multi-view stereo for large-scale scene reconstruction with MRF-based depth inference
inferredcites →https://doi.org/10.1016/j.image.2021.116417openalex-extracted
Relative multiscale deep depth from focus
inferredcites →https://doi.org/10.1016/j.neucom.2020.12.089openalex-extracted
Deep learning for monocular depth estimation: A review
inferredcites →https://doi.org/10.1049/ipr2.12877openalex-extracted
Multiscale and multidirection depth map super resolution with semantic inference
inferredcites →https://doi.org/10.1109/3dv.2016.32openalex-extracted
Deeper Depth Prediction with Fully Convolutional Residual Networks
inferredcites →https://doi.org/10.1109/access.2019.2894651openalex-extracted
Super-Resolution for Monocular Depth Estimation With Multi-Scale Sub-Pixel Convolutions and a Smoothness Constraint
inferredcites →https://doi.org/10.1109/access.2020.3006704openalex-extracted
Monocular Depth Prediction With Residual DenseASPP Network
inferredcites →https://doi.org/10.1109/access.2021.3117140openalex-extracted
Depth Map Reconstruction and Enhancement With Local and Patch Manifold Regularized Deep Depth Priors
inferredcites →https://doi.org/10.1109/cvpr.2015.7298655openalex-extracted
SUN RGB-D: A RGB-D scene understanding benchmark suite
inferredcites →https://doi.org/10.1109/cvpr.2015.7299152openalex-extracted
Deep convolutional neural fields for depth estimation from a single image
inferredcites →https://doi.org/10.1109/cvpr.2016.207openalex-extracted
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
inferredcites →https://doi.org/10.1109/cvpr.2016.308openalex-extracted
Rethinking the Inception Architecture for Computer Vision
inferredcites →https://doi.org/10.1109/cvpr.2017.238openalex-extracted
Semi-Supervised Deep Learning for Monocular Depth Map Prediction
inferredcites →https://doi.org/10.1109/cvpr.2017.243openalex-extracted
Densely Connected Convolutional Networks
inferredcites →https://doi.org/10.1109/cvpr.2017.25openalex-extracted
Multi-scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation
inferredcites →https://doi.org/10.1109/cvpr.2017.261openalex-extracted
ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes
inferredcites →https://doi.org/10.1109/cvpr.2017.634openalex-extracted
Aggregated Residual Transformations for Deep Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2017.699openalex-extracted
Unsupervised Monocular Depth Estimation with Left-Right Consistency
inferredcites →https://doi.org/10.1109/cvpr.2018.00042openalex-extracted
Single-Image Depth Estimation Based on Fourier Domain Analysis
inferredcites →https://doi.org/10.1109/cvpr.2018.00214openalex-extracted
Deep Ordinal Regression Network for Monocular Depth Estimation
inferredcites →https://doi.org/10.1109/cvpr.2018.00255openalex-extracted
Deep Layer Aggregation
inferredcites →https://doi.org/10.1109/cvpr.2018.00388openalex-extracted
DenseASPP for Semantic Segmentation in Street Scenes
inferredcites →https://doi.org/10.1109/cvpr.2018.00474openalex-extracted
MobileNetV2: Inverted Residuals and Linear Bottlenecks
inferredcites →https://doi.org/10.1109/cvpr.2019.00457openalex-extracted
TextureNet: Consistent Local Parametrizations for Learning From High-Resolution Signals on Meshes
inferredcites →https://doi.org/10.1109/iccv.2017.365openalex-extracted
A Two-Streamed Network for Estimating Fine-Scaled Depth Maps from Single RGB Images
inferredcites →https://doi.org/10.1109/iccv48922.2021.01196openalex-extracted
Vision Transformers for Dense Prediction
inferredcites →https://doi.org/10.1109/tap.2021.3111299openalex-extracted
Accurate Modeling of Antenna Structures by Means of Domain Confinement and Pyramidal Deep Neural Networks
inferredcites →https://doi.org/10.1109/tcsvt.2017.2740321openalex-extracted
Estimating Depth From Monocular Images as Classification Using Deep Fully Convolutional Residual Networks
inferredcites →https://doi.org/10.1109/tcsvt.2021.3049869openalex-extracted
Monocular Depth Estimation Using Laplacian Pyramid-Based Depth Residuals
inferredcites →https://doi.org/10.1109/tim.2023.3318748openalex-extracted
Multiscale Depth Fusion With Contextual Hybrid Enhancement Network for Image Dehazing
inferredcites →https://doi.org/10.1109/tip.2022.3167307openalex-extracted
Self-Supervised Monocular Depth Estimation With Multiscale Perception
inferredcites →https://doi.org/10.1109/wacv.2019.00116openalex-extracted
Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps With Accurate Object Boundaries
inferredcites →https://doi.org/10.1145/2964284.2967209openalex-extracted
Multi-Scale Triplet CNN for Person Re-Identification
inferredcites →https://doi.org/10.1177/0278364913491297openalex-extracted
Vision meets robotics: The KITTI dataset
inferredcites →https://doi.org/10.24963/ijcai.2018/95openalex-extracted
R³Net: Recurrent Residual Refinement Network for Saliency Detection
inferredcites →https://doi.org/10.24963/ijcai.2019/98openalex-extracted
Structure-Aware Residual Pyramid Network for Monocular Depth Estimation
inferredcites →https://doi.org/10.3390/e25060836openalex-extracted
Multiscale Attention Fusion for Depth Map Super-Resolution Generative Adversarial Networks
inferredcites →https://doi.org/10.3390/rs14122906openalex-extracted
Encoder-Decoder Structure with Multiscale Receptive Field Block for Unsupervised Depth Estimation from Monocular Video
inferredcites →https://doi.org/10.3390/s21206780openalex-extracted
DCPNet: A Densely Connected Pyramid Network for Monocular Depth Estimation
inferredcites →https://doi.org/10.48550/arxiv.1406.2283openalex-extracted
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-extracted
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1605.07081openalex-extracted
Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions
inferredcites →https://doi.org/10.48550/arxiv.1607.00730openalex-extracted
A Two-Streamed Network for Estimating Fine-Scaled Depth Maps from Single RGB Images
inferredcites →https://doi.org/10.48550/arxiv.1711.05101openalex-extracted
Decoupled Weight Decay Regularization
inferredcites →https://doi.org/10.48550/arxiv.1907.10326openalex-extracted
From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation
inferredcites →https://doi.org/10.48550/arxiv.2111.02444openalex-extracted
Panoptic 3D Scene Reconstruction From a Single RGB Image
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.