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Adaptive LiDAR Sampling and Depth Completion Using Ensemble Variance

https://doi.org/10.1109/tip.2021.3120042

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What this work says

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

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Estimating Depth from RGB and Sparse Sensing

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Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network

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Active Learning for Regression Based on Query by Committee

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Cross-validation based single response adaptive design of experiments for Kriging metamodeling of deterministic computer simulations

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A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design

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Geometry in active learning for binary and multi-class image segmentation

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Active semi-supervised learning with multiple complementary information

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Evidential query-by-committee active learning for pedestrian detection in high-density crowds

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Query-by-committee improvement with diversity and density in batch active learning

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Adaptive sampling for compressed sensing based image compression

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Selective Sampling Using the Query by Committee Algorithm

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Random Forests

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Large-scale nanophotonic phased array

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Subwavelength integrated photonics

inferredcites →https://doi.org/10.1049/el.2018.6149openalex-snapshot

Multi‐scale features fusion from sparse LiDAR data and single image for depth completion

inferredcites →https://doi.org/10.1109/3dv.2016.15openalex-snapshot

Robust Recovery of Heavily Degraded Depth Measurements

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

Sparsity Invariant CNNs

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The farthest point strategy for progressive image sampling

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Deep Architecture With Cross Guidance Between Single Image and Sparse LiDAR Data for Depth Completion

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

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

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Stereoscopic inpainting: Joint color and depth completion from stereo images

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Layer Depth Denoising and Completion for Structured-Light RGB-D Cameras

inferredcites →https://doi.org/10.1109/cvpr.2014.433openalex-snapshot

Depth Enhancement via Low-Rank Matrix Completion

inferredcites →https://doi.org/10.1109/cvpr.2015.7298837openalex-snapshot

Sparse depth super resolution

inferredcites →https://doi.org/10.1109/cvpr.2019.00287openalex-snapshot

Learning to Sample

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/cvprw.2015.7301378openalex-snapshot

Active learning approach to detecting standing dead trees from ALS point clouds combined with aerial infrared imagery

inferredcites →https://doi.org/10.1109/iccp48838.2020.9105252openalex-snapshot

Deep Adaptive LiDAR: End-to-end Optimization of Sampling and Depth Completion at Low Sampling Rates

inferredcites →https://doi.org/10.1109/iccv.2011.6126488openalex-snapshot

Dense disparity maps from sparse disparity measurements

inferredcites →https://doi.org/10.1109/iccv.2019.00290openalex-snapshot

Depth Completion From Sparse LiDAR Data With Depth-Normal Constraints

inferredcites →https://doi.org/10.1109/iccv.2019.01012openalex-snapshot

Learning Joint 2D-3D Representations for Depth Completion

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/icra.2019.8794161openalex-snapshot

Depth Completion with Deep Geometry and Context Guidance

inferredcites →https://doi.org/10.1109/icra40945.2020.9197191openalex-snapshot

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

inferredcites →https://doi.org/10.1109/iros.2016.7759040openalex-snapshot

Sparse sensing for resource-constrained depth reconstruction

inferredcites →https://doi.org/10.1109/itsc.2019.8917294openalex-snapshot

DFuseNet: Deep Fusion of RGB and Sparse Depth Information for Image Guided Dense Depth Completion

inferredcites →https://doi.org/10.1109/ivs.2019.8814236openalex-snapshot

Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector

inferredcites →https://doi.org/10.1109/prrs.2018.8486166openalex-snapshot

Integrating Active Learning and Contextually Guide for Semantic Labeling of LiDAR Point Cloud

inferredcites →https://doi.org/10.1109/tip.2014.2361034openalex-snapshot

High-Quality Depth Map Upsampling and Completion for RGB-D Cameras

inferredcites →https://doi.org/10.1109/tip.2015.2409551openalex-snapshot

Depth Reconstruction From Sparse Samples: Representation, Algorithm, and Sampling

inferredcites →https://doi.org/10.1109/tip.2019.2960589openalex-snapshot

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

inferredcites →https://doi.org/10.1109/tip.2020.3040528openalex-snapshot

Learning Guided Convolutional Network for Depth Completion

inferredcites →https://doi.org/10.1109/tmm.2019.2958760openalex-snapshot

Adaptive Image Sampling Using Deep Learning and Its Application on X-Ray Fluorescence Image Reconstruction

inferredcites →https://doi.org/10.1109/tnnls.2018.2868649openalex-snapshot

Pool-Based Sequential Active Learning for Regression

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

Confidence Propagation through CNNs for Guided Sparse Depth Regression

inferredcites →https://doi.org/10.1109/wacv45572.2020.9093349openalex-snapshot

Depth Completion via Deep Basis Fitting

inferredcites →https://doi.org/10.1117/1.3463013openalex-snapshot

Depth map refinement using multiple patch-based depth image completion via local stereo warping

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inferredcites →https://doi.org/10.1364/ol.42.004091openalex-snapshot

Coherent solid-state LIDAR with silicon photonic optical phased arrays

inferredcites →https://doi.org/10.15607/rss.2018.xiv.011openalex-snapshot

Directionally Controlled Time-of-Flight Ranging for Mobile Sensing Platforms

inferredcites →https://doi.org/10.1561/2200000037openalex-snapshot

Theory of Disagreement-Based Active Learning

inferredcites →https://doi.org/10.1613/jair.295openalex-snapshot

Active Learning with Statistical Models

inferredcites →https://doi.org/10.21236/ada307061openalex-snapshot

Minimizing Statistical Bias with Queries.

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

Sparse and Noisy LiDAR Completion with RGB Guidance and Uncertainty

inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot

Scikit-learn: Machine Learning in Python

inferredcites →https://doi.org/10.48550/arxiv.1703.03365openalex-snapshot

Learning Active Learning from Data

What points at this work

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

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inferred← citeshttps://doi.org/10.1007/978-3-031-20086-1_3openalex-snapshot

Monitored Distillation for Positive Congruent Depth Completion

inferred← citeshttps://doi.org/10.1007/s11263-022-01726-1openalex-snapshot

Project to Adapt: Domain Adaptation for Depth Completion from Noisy and Sparse Sensor Data

inferred← citeshttps://doi.org/10.1016/j.imavis.2024.105227openalex-snapshot

Coplane-constrained sparse depth sampling and local depth propagation for depth estimation

inferred← citeshttps://doi.org/10.1109/jsen.2025.3526733openalex-snapshot

ELLAS: Enhancing LiDAR Perception With Location-Aware Scanning Profile Adaptation

inferred← citeshttps://doi.org/10.1109/jsen.2026.3722238openalex-snapshot

Location-Based Foveation in MIMO Radars: Joint Angular Resolution and Range Adaptation

inferred← citeshttps://doi.org/10.1109/lra.2022.3142738openalex-snapshot

End-to-End Optimization of LiDAR Beam Configuration for 3D Object Detection and Localization

inferred← citeshttps://doi.org/10.1109/sisy56759.2022.10036269openalex-snapshot

Multi-modal RGBD Attention Fusion for Dense Depth Estimation

inferred← citeshttps://doi.org/10.1109/tci.2022.3150974openalex-snapshot

Fast Task-Based Adaptive Sampling for 3D Single-Photon Multispectral Lidar Data

inferred← citeshttps://doi.org/10.1109/tcsvt.2023.3312664openalex-snapshot

SPTR: Structure-Preserving Transformer for Unsupervised Indoor Depth Completion

inferred← citeshttps://doi.org/10.1109/tim.2024.3420352openalex-snapshot

Progressive Depth Decoupling and Modulating for Flexible Depth Completion

inferred← citeshttps://doi.org/10.1109/tim.2025.3593596openalex-snapshot

Sparse Aerial Ground Height Recovery Using Normalized Weighted Nonlocal Laplacian

inferred← citeshttps://doi.org/10.1109/tip.2023.3343105openalex-snapshot

Active Disparity Sampling for Stereo Matching With Adjoint Network

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

Structure-Aware Cross-Modal Transformer for Depth Completion

inferred← citeshttps://doi.org/10.1109/wacv56688.2023.00304openalex-snapshot

Uncertainty-Aware Interactive LiDAR Sampling for Deep Depth Completion

inferred← citeshttps://doi.org/10.1115/1.4065898openalex-snapshot

Solid-State LiDAR With Optimized Dynamic Beam Power Intensity for Reduced Thermal Load

inferred← citeshttps://doi.org/10.1364/ao.482535openalex-snapshot

Adaptive point cloud acquisition and upsampling for automotive lidar

inferred← citeshttps://doi.org/10.3390/s25175495openalex-snapshot

GAC-Net: A Geometric–Attention Fusion Network for Sparse Depth Completion from LiDAR and Image

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

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