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HMS-Net: Hierarchical Multi-Scale Sparsity-Invariant Network for Sparse Depth Completion

https://doi.org/10.1109/tip.2019.2960589

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What this work says

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

inferredcites →https://doi.org/10.1007/978-3-030-01267-0_17openalex-snapshot

Conditional Prior Networks for Optical Flow

inferredcites →https://doi.org/10.1007/978-3-030-01270-0_7openalex-snapshot

Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network

inferredcites →https://doi.org/10.1007/978-3-030-20887-5_31openalex-snapshot

Deep Convolutional Compressed Sensing for LiDAR Depth Completion

inferredcites →https://doi.org/10.1007/978-3-319-24574-4_28openalex-snapshot

U-Net: Convolutional Networks for Biomedical Image Segmentation

inferredcites →https://doi.org/10.1007/978-3-319-45886-1_4openalex-snapshot

Semantically Guided Depth Upsampling

inferredcites →https://doi.org/10.1007/978-3-319-46484-8_29openalex-snapshot

Stacked Hourglass Networks for Human Pose Estimation

inferredcites →https://doi.org/10.1007/978-3-319-46487-9_17openalex-snapshot

ATGV-Net: Accurate Depth Super-Resolution

inferredcites →https://doi.org/10.1007/978-3-319-46487-9_22openalex-snapshot

Depth Map Super-Resolution by Deep Multi-Scale Guidance

inferredcites →https://doi.org/10.1007/978-3-319-46487-9_38openalex-snapshot

The Fast Bilateral Solver

inferredcites →https://doi.org/10.1007/978-3-319-46493-0_10openalex-snapshot

Deep Joint Image Filtering

inferredcites →https://doi.org/10.1007/978-3-642-33712-3_6openalex-snapshot

Patch Based Synthesis for Single Depth Image Super-Resolution

inferredcites →https://doi.org/10.1007/978-3-642-33715-4_54openalex-snapshot

Indoor Segmentation and Support Inference from RGBD Images

inferredcites →https://doi.org/10.1109/3dtv.2011.5877202openalex-snapshot

Temporal filtering for depth maps generated by Kinect depth camera

inferredcites →https://doi.org/10.1109/3dv.2016.32openalex-snapshot

Deeper Depth Prediction with Fully Convolutional Residual Networks

inferredcites →https://doi.org/10.1109/3dv.2017.00012openalex-snapshot

Sparsity Invariant CNNs

inferredcites →https://doi.org/10.1109/3dv.2018.00017openalex-snapshot

Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentation

inferredcites →https://doi.org/10.1109/crv.2018.00013openalex-snapshot

In Defense of Classical Image Processing: Fast Depth Completion on the CPU

inferredcites →https://doi.org/10.1109/cvpr.2016.350openalex-snapshot

The Cityscapes Dataset for Semantic Urban Scene Understanding

inferredcites →https://doi.org/10.1109/cvpr.2017.106openalex-snapshot

Feature Pyramid Networks for Object Detection

inferredcites →https://doi.org/10.1109/cvpr.2017.353openalex-snapshot

Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes

inferredcites →https://doi.org/10.1109/cvpr.2017.660openalex-snapshot

Pyramid Scene Parsing Network

inferredcites →https://doi.org/10.1109/cvpr.2018.00026openalex-snapshot

Deep Depth Completion of a Single RGB-D Image

inferredcites →https://doi.org/10.1109/cvpr.2018.00908openalex-snapshot

SBNet: Sparse Blocks Network for Fast Inference

inferredcites →https://doi.org/10.1109/cvpr.2019.00343openalex-snapshot

DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene From Sparse LiDAR Data and Single Color Image

inferredcites →https://doi.org/10.1109/cvpr.2019.00347openalex-snapshot

Dense Depth Posterior (DDP) From Single Image and Sparse Range

inferredcites →https://doi.org/10.1109/iccv.2013.127openalex-snapshot

Image Guided Depth Upsampling Using Anisotropic Total Generalized Variation

inferredcites →https://doi.org/10.1109/iccv.2015.66openalex-snapshot

Variational Depth Superresolution Using Example-Based Edge Representations

inferredcites →https://doi.org/10.1109/icra.2017.7989590openalex-snapshot

Parse geometry from a line: Monocular depth estimation with partial laser observation

inferredcites →https://doi.org/10.1109/icra.2018.8460184openalex-snapshot

Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image

inferredcites →https://doi.org/10.1109/icra.2019.8793637openalex-snapshot

Self-Supervised Sparse-to-Dense: Self-Supervised Depth Completion from LiDAR and Monocular Camera

inferredcites →https://doi.org/10.1109/ivs.2017.7995966openalex-snapshot

Efficient ConvNet for real-time semantic segmentation

inferredcites →https://doi.org/10.1109/tbc.2018.2818405openalex-snapshot

Global Auto-Regressive Depth Recovery via Iterative Non-Local Filtering

inferredcites →https://doi.org/10.1109/tip.2014.2329776openalex-snapshot

Color-Guided Depth Recovery From RGB-D Data Using an Adaptive Autoregressive Model

inferredcites →https://doi.org/10.1109/tip.2015.2501749openalex-snapshot

Edge-Guided Single Depth Image Super Resolution

inferredcites →https://doi.org/10.1109/tip.2018.2832296openalex-snapshot

Learning Depth From Single Images With Deep Neural Network Embedding Focal Length

inferredcites →https://doi.org/10.1109/tip.2018.2887029openalex-snapshot

Hierarchical Features Driven Residual Learning for Depth Map Super-Resolution

inferredcites →https://doi.org/10.1109/tmm.2015.2457678openalex-snapshot

Joint Super Resolution and Denoising From a Single Depth Image

inferredcites →https://doi.org/10.1109/tpami.2019.2929170openalex-snapshot

Confidence Propagation through CNNs for Guided Sparse Depth Regression

inferredcites →https://doi.org/10.1117/12.911909openalex-snapshot

Efficient spatio-temporal hole filling strategy for Kinect depth maps

inferredcites →https://doi.org/10.1137/1109020openalex-snapshot

On Estimating Regression

inferredcites →https://doi.org/10.23919/mva.2019.8757939openalex-snapshot

Sparse and Noisy LiDAR Completion with RGB Guidance and Uncertainty

inferredcites →https://doi.org/10.4230/oasics.dx.2026.15openalex-snapshot

AutoResearch Lite: a reproducible paper-inspired experiment runner (Adam mechanism reproduction and convergence-speed comparison)

inferredcites →https://doi.org/10.48550/arxiv.1406.2283openalex-snapshot

Depth Map Prediction from a Single Image using a Multi-Scale Deep Network

inferredcites →https://doi.org/10.48550/arxiv.1502.03167openalex-snapshot

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

inferredcites →https://doi.org/10.48550/arxiv.1805.11913openalex-snapshot

Propagating Confidences through CNNs for Sparse Data Regression

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Reversing the Cycle: Self-supervised Deep Stereo Through Enhanced Monocular Distillation

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Efficient Depth Completion Network Based on Dynamic Gated Fusion

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Data Fusion for Human Activity Recognition Based on RF Sensing and IMU Sensor

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GraphCSPN: Geometry-Aware Depth Completion via Dynamic GCNs

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Monitored Distillation for Positive Congruent Depth Completion

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LSMD-Net: LiDAR-Stereo Fusion with Mixture Density Network for Depth Sensing

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Bridging Depth Estimation and Completion for Mobile Robots Reliable 3D Perception

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Learning to Estimate Two Dense Depths from LiDAR and Event Data

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Performance Evaluation of Depth Completion Neural Networks for Various RGB-D Camera Technologies in Indoor Scenarios

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RoofDiffusion: Constructing Roofs from Severely Corrupted Point Data via Diffusion

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Hole-Filling Method Using Nonlocal Non-convex Regularization for Consumer Depth Cameras

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4D mmWave Radar Depth Completion for Water Scenes via Monodepth-Guided Initialization

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Deep Unrolled Weighted Graph Laplacian Regularization for Depth Completion

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RCDformer: Transformer-based dense depth estimation by sparse radar and camera

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Wasserstein Generative Adversarial Network for Depth Completion With Anisotropic Diffusion Depth Enhancement

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AGNet: Attention Guided Sparse Depth Completion Using Convolutional Neural Networks

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Saving Bits Using Multi-Sensor Collaboration

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MASC-Net: Multi-scale Anisotropic Sparse Convolutional Network for Sparse Depth Densification

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From Depth What Can You See? Depth Completion via Auxiliary Image Reconstruction

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Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End

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Depth Estimation from Camera Image and mmWave Radar Point Cloud

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Test- Time Adaptation for Depth Completion

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Real-Time Depth Estimation with an Optimized Encoder-Decoder Architecture on Embedded Devices

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Test-Time Prompt Tuning for Zero-Shot Depth Completion

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Deformable Spatial Propagation Networks For Depth Completion

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SADG-Net: Sparse Adaptive Dynamic Guidance Network for Depth Completion

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Selective Progressive Learning for Sparse Depth Completion

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Depth Completion via A Dual-Fusion Method

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LiStereo: Generate Dense Depth Maps from LIDAR and Stereo Imagery

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Super-Pixel Sampler: a Data-driven Approach for Depth Sampling and Reconstruction

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SEHLNet: Separate Estimation of High- and Low-Frequency components for Depth Completion

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PENet: Towards Precise and Efficient Image Guided Depth Completion

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MDANet: Multi-Modal Deep Aggregation Network for Depth Completion

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Self-Guided Instance-Aware Network for Depth Completion and Enhancement

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Multiscale Adaptation Fusion Networks for Depth Completion

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Deep Depth Estimation from Visual-Inertial SLAM

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Self Attention Guided Depth Completion using RGB and Sparse LiDAR Point Clouds

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All-day Depth Completion

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An Intelligent Implementation of Multi-Sensing Data Fusion With Neuromorphic Computing for Human Activity Recognition

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Self-Supervised Depth Completion With Calibration Enhancement and Normal Guidance

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Dense Depth Map Estimation Based on Camera–LiDAR Sensor Fusion

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Unsupervised Depth Completion From Visual Inertial Odometry

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Discontinuous and Smooth Depth Completion With Binary Anisotropic Diffusion Tensor

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Learning Topology From Synthetic Data for Unsupervised Depth Completion

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An Adaptive Framework for Learning Unsupervised Depth Completion

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Deep Cross Spectral Stereo Matching Using Multi-Spectral Image Fusion

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Struct-MDC: Mesh-Refined Unsupervised Depth Completion Leveraging Structural Regularities From Visual SLAM

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CU-Net: LiDAR Depth-Only Completion With Coupled U-Net

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Guided Spatial Propagation Network for Depth Completion

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Expanding Sparse LiDAR Depth and Guiding Stereo Matching for Robust Dense Depth Estimation

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Monocular Depth Estimation With Multi-Scale Feature Fusion

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Self-Paced Knowledge Distillation for Real-Time Image Guided Depth Completion

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Depth-Independent Depth Completion via Least Square Estimation

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Stereo-Assisted Monitored Distillation for Accurate Depth Completion

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SDC: Spatial Depth Completion for Outdoor Scenes

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Learning Modal and Spatial Features With Lightweight 3D Convolution for RGB Guided Depth Completion

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Panorama-LiDAR Fusion for Dense Omnidirectional Depth Completion in 3D Street Scene

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S2DNet: Depth Estimation From Single Image and Sparse Samples

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Multi-Level Fusion and Attention-Guided CNN for Image Dehazing

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Deep Unfolding Network for Image Desnowing With Snow Shape Prior

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Stage-Wise Affinity Evolution Spatial Propagation Network for Depth Completion

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GeoDC: Geometry-Constrained Depth Completion With Depth Distribution Modeling

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Dense Depth-Map Estimation Based on Fusion of Event Camera and Sparse LiDAR

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GMDepth: Depth Completion via Global Dense-to-Sparse Matching

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CMNC-Net: A Cross-Modal Dual-Branch Network With Noise Awareness for Depth Completion in Transparent Scenes

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Learning Guided Convolutional Network for Depth Completion

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2020 Index IEEE Transactions on Image Processing Vol. 29

inferred← citeshttps://doi.org/10.1109/tip.2021.3055629openalex-snapshot

Learning Steering Kernels for Guided Depth Completion

inferred← citeshttps://doi.org/10.1109/tip.2021.3079821openalex-snapshot

Adaptive Context-Aware Multi-Modal Network for Depth Completion

inferred← citeshttps://doi.org/10.1109/tip.2021.3120042openalex-snapshot

Adaptive LiDAR Sampling and Depth Completion Using Ensemble Variance

inferred← citeshttps://doi.org/10.1109/tip.2024.3355807openalex-snapshot

Structure-Aware Cross-Modal Transformer for Depth Completion

inferred← citeshttps://doi.org/10.1109/tip.2025.3597047openalex-snapshot

Propagating Sparse Depth via Depth Foundation Model for Out-of-Distribution Depth Completion

inferred← citeshttps://doi.org/10.1109/tits.2021.3106055openalex-snapshot

Self-Supervised Depth Completion From Direct Visual-LiDAR Odometry in Autonomous Driving

inferred← citeshttps://doi.org/10.1109/tits.2021.3129901openalex-snapshot

LiDAR Depth Completion Using Color-Embedded Information via Knowledge Distillation

inferred← citeshttps://doi.org/10.1109/tits.2025.3528017openalex-snapshot

High-Resolution LiDAR Depth Completion Algorithm Guided by Image Topography Maps

inferred← citeshttps://doi.org/10.1109/tiv.2024.3396174openalex-snapshot

Towards Better Unguided Depth Completion via Cross-Modality Knowledge Distillation in the Frequency Domain

inferred← citeshttps://doi.org/10.1109/tiv.2024.3511923openalex-snapshot

VIL-PPGen: A Novel Pseudo Point Generator Based on Visible Light Camera, Infrared Camera and Lidar

inferred← citeshttps://doi.org/10.1109/tmm.2023.3289752openalex-snapshot

SGSR-Net: Structure Semantics Guided LiDAR Super-Resolution Network for Indoor LiDAR SLAM

inferred← citeshttps://doi.org/10.1109/tnnls.2021.3129801openalex-snapshot

Distance Transform Pooling Neural Network for LiDAR Depth Completion

inferred← citeshttps://doi.org/10.1109/tnnls.2022.3201534openalex-snapshot

Recent Advances in Conventional and Deep Learning-Based Depth Completion: A Survey

inferred← citeshttps://doi.org/10.1109/tpami.2019.2929170openalex-snapshot

Confidence Propagation through CNNs for Guided Sparse Depth Regression

inferred← citeshttps://doi.org/10.1109/tpami.2022.3229090openalex-snapshot

Deep Depth Completion from Extremely Sparse Data: A Survey

inferred← citeshttps://doi.org/10.1109/tpami.2025.3581311openalex-snapshot

Robust Depth Estimation Under Sensor Degradations: A Multi-Sensor Fusion Perspective

inferred← citeshttps://doi.org/10.1109/tvcg.2026.3665530openalex-snapshot

Self-Supervised Depth Completion Guided by 3D Perception and Geometry Consistency

inferred← citeshttps://doi.org/10.1109/wacv45572.2020.9093407openalex-snapshot

A Multi-Scale Guided Cascade Hourglass Network for Depth Completion

inferred← citeshttps://doi.org/10.1109/wacv56688.2023.00577openalex-snapshot

SIUNet: Sparsity Invariant U-Net for Edge-Aware Depth Completion

inferred← citeshttps://doi.org/10.1109/wacvw54805.2022.00012openalex-snapshot

Depth Completion Auto-Encoder

inferred← citeshttps://doi.org/10.1145/3503161.3548418openalex-snapshot

Robust Multimodal Depth Estimation using Transformer based Generative Adversarial Networks

inferred← citeshttps://doi.org/10.1145/3662007openalex-snapshot

Proceedings of the Workshop on Adaptive AIoT Systems

inferred← citeshttps://doi.org/10.1145/3662007.3663882openalex-snapshot

Robust Control of Quadruped Robots using Reinforcement Learning and Depth Completion Network

inferred← citeshttps://doi.org/10.1145/3677327openalex-snapshot

Deep Learning-based Depth Estimation Methods from Monocular Image and Videos: A Comprehensive Survey

inferred← citeshttps://doi.org/10.11606/d.18.2021.tde-02092021-110320openalex-snapshot

Aprendizagem profunda aplicada a estimação monocular de profundidade: uma abordagem baseada em atenção e complementação

inferred← citeshttps://doi.org/10.3389/fnbot.2023.1124676openalex-snapshot

Real-time depth completion based on LiDAR-stereo for autonomous driving

inferred← citeshttps://doi.org/10.3390/app13179804openalex-snapshot

Depth Completion in Autonomous Driving: Adaptive Spatial Feature Fusion and Semi-Quantitative Visualization

inferred← citeshttps://doi.org/10.3390/electronics11142222openalex-snapshot

WeaveNet: Solution for Variable Input Sparsity Depth Completion

inferred← citeshttps://doi.org/10.3390/electronics13122418openalex-snapshot

NSVDNet: Normalized Spatial-Variant Diffusion Network for Robust Image-Guided Depth Completion

inferred← citeshttps://doi.org/10.3390/en15134681openalex-snapshot

Adaptation of Grad-CAM Method to Neural Network Architecture for LiDAR Pointcloud Object Detection

inferred← citeshttps://doi.org/10.3390/math13060953openalex-snapshot

EDRNet: Edge-Enhanced Dynamic Routing Adaptive for Depth Completion

inferred← citeshttps://doi.org/10.3390/rs13183780openalex-snapshot

A Physics-Driven CNN Model for Real-Time Sea Waves 3D Reconstruction

inferred← citeshttps://doi.org/10.3390/rs14194689openalex-snapshot

An Efficient Information-Reinforced Lidar Deep Completion Network without RGB Guided

inferred← citeshttps://doi.org/10.3390/rs15010135openalex-snapshot

Self-Supervised Depth Completion Based on Multi-Modal Spatio-Temporal Consistency

inferred← citeshttps://doi.org/10.3390/rs15061625openalex-snapshot

Accurate and Serialized Dense Point Cloud Reconstruction for Aerial Video Sequences

inferred← citeshttps://doi.org/10.3390/rs15143686openalex-snapshot

Depth Information Precise Completion-GAN: A Precisely Guided Method for Completing Ill Regions in Depth Maps

inferred← citeshttps://doi.org/10.3390/s22124603openalex-snapshot

An Adaptive Fusion Algorithm for Depth Completion

inferred← citeshttps://doi.org/10.3390/s22176414openalex-snapshot

SGSNet: A Lightweight Depth Completion Network Based on Secondary Guidance and Spatial Fusion

inferred← citeshttps://doi.org/10.3390/s22197533openalex-snapshot

LiDAR Intensity Completion: Fully Exploiting the Message from LiDAR Sensors

inferred← citeshttps://doi.org/10.3390/s24196270openalex-snapshot

A Transformer-Based Image-Guided Depth-Completion Model with Dual-Attention Fusion Module

inferred← citeshttps://doi.org/10.36227/techrxiv.24014946openalex-snapshot

Bridging the Gap Between Indoor Depth Completion and Masked Autoencoders

inferred← citeshttps://doi.org/10.36227/techrxiv.24014946.v1openalex-snapshot

Bridging the Gap Between Indoor Depth Completion and Masked Autoencoders

inferred← citeshttps://doi.org/10.48550/arxiv.1905.08616openalex-snapshot

Unsupervised Depth Completion from Visual Inertial Odometry

inferred← citeshttps://doi.org/10.48550/arxiv.2007.04251openalex-snapshot

Deformable spatial propagation network for depth completion

inferred← citeshttps://doi.org/10.48550/arxiv.2106.02994openalex-snapshot

Learning Topology from Synthetic Data for Unsupervised Depth Completion

inferred← citeshttps://doi.org/10.48550/arxiv.2106.03010openalex-snapshot

An Adaptive Framework for Learning Unsupervised Depth Completion

inferred← citeshttps://doi.org/10.5220/0010838500003124openalex-snapshot

SemSegDepth: A Combined Model for Semantic Segmentation and Depth Completion

inferred← citeshttps://doi.org/10.70675/7a5d0e4bz24baz4c91zb537z26ffa5fbaf2copenalex-snapshot

Multimodal estimation of movement and depth based on events for scene analysis

inferred← citeshttps://doi.org/10.9746/sicetr.57.37openalex-snapshot

Dynamic Obstacles Detection and 3D Reconstruction by a Monocular Camera and a 3D-map

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