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Multi-stage point completion network with critical set supervision

https://doi.org/10.1016/j.cagd.2020.101925

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What this work says

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

inferredcites →https://doi.org/10.1016/j.cag.2015.11.003openalex-snapshot

Data-driven contextual modeling for 3D scene understanding

inferredcites →https://doi.org/10.1109/3dv.2018.00088openalex-snapshot

PCN: Point Completion Network

inferredcites →https://doi.org/10.1109/cvpr.1993.341010openalex-snapshot

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

inferredcites →https://doi.org/10.1109/cvpr.2015.7298801openalex-snapshot

3D ShapeNets: A deep representation for volumetric shapes

inferredcites →https://doi.org/10.1109/cvpr.2017.16openalex-snapshot

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

inferredcites →https://doi.org/10.1109/cvpr.2017.24openalex-snapshot

Distinguishing the Indistinguishable: Exploring Structural Ambiguities via Geodesic Context

inferredcites →https://doi.org/10.1109/cvpr.2017.264openalex-snapshot

A Point Set Generation Network for 3D Object Reconstruction from a Single Image

inferredcites →https://doi.org/10.1109/cvpr.2017.693openalex-snapshot

Shape Completion Using 3D-Encoder-Predictor CNNs and Shape Synthesis

inferredcites →https://doi.org/10.1109/cvpr.2018.00029openalex-snapshot

FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation

inferredcites →https://doi.org/10.1109/cvpr.2018.00109openalex-snapshot

Pointwise Convolutional Neural Networks

inferredcites →https://doi.org/10.1109/cvpr.2018.00268openalex-snapshot

SPLATNet: Sparse Lattice Networks for Point Cloud Processing

inferredcites →https://doi.org/10.1109/cvpr.2018.00295openalex-snapshot

PU-Net: Point Cloud Upsampling Network

inferredcites →https://doi.org/10.1109/cvpr.2018.00481openalex-snapshot

ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans

inferredcites →https://doi.org/10.1109/cvpr.2018.00488openalex-snapshot

Texture Mapping for 3D Reconstruction with RGB-D Sensor

inferredcites →https://doi.org/10.1109/cvpr.2019.00047openalex-snapshot

TopNet: Structural Point Cloud Decoder

inferredcites →https://doi.org/10.1109/cvpr.2019.00099openalex-snapshot

DrivingStereo: A Large-Scale Dataset for Stereo Matching in Autonomous Driving Scenarios

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

Learning to Sample

inferredcites →https://doi.org/10.1109/cvpr.2019.00571openalex-snapshot

PointWeb: Enhancing Local Neighborhood Features for Point Cloud Processing

inferredcites →https://doi.org/10.1109/cvpr.2019.00605openalex-snapshot

RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape Completion

inferredcites →https://doi.org/10.1109/cvpr.2019.00611openalex-snapshot

Patch-Based Progressive 3D Point Set Upsampling

inferredcites →https://doi.org/10.1109/cvpr.2019.01272openalex-snapshot

PCAN: 3D Attention Map Learning Using Contextual Information for Point Cloud Based Retrieval

inferredcites →https://doi.org/10.1109/iccv.1995.466933openalex-snapshot

Proceedings of IEEE International Conference on Computer Vision

inferredcites →https://doi.org/10.1109/iccv.2005.221openalex-snapshot

Shape from symmetry

inferredcites →https://doi.org/10.1109/iccv.2017.19openalex-snapshot

High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference

inferredcites →https://doi.org/10.1109/iccv.2019.00730openalex-snapshot

PU-GAN: A Point Cloud Upsampling Adversarial Network

inferredcites →https://doi.org/10.1109/iccvw.2017.86openalex-snapshot

3D Object Reconstruction from a Single Depth View with Adversarial Learning

inferredcites →https://doi.org/10.1109/icra.2011.5980567openalex-snapshot

3D is here: Point Cloud Library (PCL)

inferredcites →https://doi.org/10.1109/iros.2017.8206060openalex-snapshot

Shape completion enabled robotic grasping

inferredcites →https://doi.org/10.1109/smi.2004openalex-snapshot

Proceedings Shape Modeling Applications, 2004.

inferredcites →https://doi.org/10.1109/smi.2004.1314506openalex-snapshot

Least-squares meshes

inferredcites →https://doi.org/10.1109/tvcg.2016.2553102openalex-snapshot

Shape Completion from a Single RGBD Image

inferredcites →https://doi.org/10.1111/1467-8659.00652openalex-snapshot

COMPUTER GRAPHICS forum

inferredcites →https://doi.org/10.1111/cgf.12573openalex-snapshot

Database‐Assisted Object Retrieval for Real‐Time 3D Reconstruction

inferredcites →https://doi.org/10.1111/cgf.12998openalex-snapshot

Geometrically Based Linear Iterative Clustering for Quantitative Feature Correspondence

inferredcites →https://doi.org/10.1111/cgf.13841openalex-snapshot

Pyramid Multi‐View Stereo with Local Consistency

inferredcites →https://doi.org/10.1145/1141911.1141924openalex-snapshot

Partial and approximate symmetry detection for 3D geometry

inferredcites →https://doi.org/10.1145/1174429.1174494openalex-snapshot

Laplacian mesh optimization

inferredcites →https://doi.org/10.1145/1399504.1360642openalex-snapshot

Discovering structural regularity in 3D geometry

inferredcites →https://doi.org/10.1145/2366145.2366157openalex-snapshot

Acquiring 3D indoor environments with variability and repetition

inferredcites →https://doi.org/10.1145/2487228.2487237openalex-snapshot

Screened poisson surface reconstruction

inferredcites →https://doi.org/10.1609/aaai.v32i1.11691openalex-snapshot

Adaptive Graph Convolutional Neural Networks

inferredcites →https://doi.org/10.1609/aaai.v32i1.12278openalex-snapshot

Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction

inferredcites →https://doi.org/10.1609/aaai.v33i01.33018819openalex-snapshot

CAPNet: Continuous Approximation Projection for 3D Point Cloud Reconstruction Using 2D Supervision

inferredcites →https://doi.org/10.48550/arxiv.1612.00593openalex-snapshot

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

What points at this work

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

inferred← citeshttps://doi.org/10.2139/ssrn.4654384openalex-extracted

inferred← citeshttps://doi.org/10.1007/978-981-92-3501-8_17openalex-snapshot

Respiratory Motion Compensation Based on Mid-axis Plane for Dynamic Human Point Cloud Inpainting

inferred← citeshttps://doi.org/10.1007/s11042-021-11433-7openalex-snapshot

3D-ARNet: An accurate 3D point cloud reconstruction network from a single-image

inferred← citeshttps://doi.org/10.1016/j.cag.2023.12.012openalex-snapshot

Voting-based patch sequence autoregression network for adaptive point cloud completion

inferred← citeshttps://doi.org/10.1016/j.cagd.2025.102456openalex-snapshot

Detail-preserving shape completion of point cloud models with articulated structure

inferred← citeshttps://doi.org/10.1016/j.gmod.2024.101233openalex-snapshot

Deep-learning-based point cloud completion methods: A review

inferred← citeshttps://doi.org/10.1016/j.imavis.2025.105735openalex-snapshot

CDAF: Cross-Modal and Dual-channel Upsample Adaptive Fusion network for Point Cloud Completion

inferred← citeshttps://doi.org/10.1016/j.neucom.2024.127790openalex-snapshot

CompleteDT: Point cloud completion with information-perception transformers

inferred← citeshttps://doi.org/10.1016/j.neucom.2024.129037openalex-snapshot

Enhancing Performance of 3D Point Completion Network using Consistency Loss

inferred← citeshttps://doi.org/10.1088/1361-6501/ae081dopenalex-snapshot

Detection of fracture crack in carbon fiber reinforced polymer (CFRP) cables based on microfocus x-ray imaging

inferred← citeshttps://doi.org/10.1109/iccv48922.2021.01127openalex-snapshot

MSR-GCN: Multi-Scale Residual Graph Convolution Networks for Human Motion Prediction

inferred← citeshttps://doi.org/10.1109/iccv51070.2023.01702openalex-snapshot

Learning Long-range Information with Dual-Scale Transformers for Indoor Scene Completion

inferred← citeshttps://doi.org/10.1109/lra.2023.3310406openalex-snapshot

DualGenerator: Information Interaction-Based Generative Network for Point Cloud Completion

inferred← citeshttps://doi.org/10.1109/tcsvt.2022.3204771openalex-snapshot

Projected Generative Adversarial Network for Point Cloud Completion

inferred← citeshttps://doi.org/10.1109/tgrs.2021.3105551openalex-snapshot

Dense Point Cloud Completion Based on Generative Adversarial Network

inferred← citeshttps://doi.org/10.1109/tits.2022.3195555openalex-snapshot

Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysis

inferred← citeshttps://doi.org/10.1109/tpami.2025.3647855openalex-snapshot

Diffusion-Driven Self-Supervised Learning for Shape Reconstruction and Pose Estimation

inferred← citeshttps://doi.org/10.1109/tvcg.2022.3185247openalex-snapshot

Point Cloud Completion Via Skeleton-Detail Transformer

inferred← citeshttps://doi.org/10.1109/tvcg.2023.3344935openalex-snapshot

Point Cloud Completion: A Survey

inferred← citeshttps://doi.org/10.1145/3503161.3547777openalex-snapshot

PC 2 -PU: Patch Correlation and Point Correlation for Effective Point Cloud Upsampling

inferred← citeshttps://doi.org/10.1145/3581783.3611716openalex-snapshot

SD-Net: Spatially-Disentangled Point Cloud Completion Network

inferred← citeshttps://doi.org/10.1145/3664647.3680590openalex-snapshot

Reverse2Complete: Unpaired Multimodal Point Cloud Completion via Guided Diffusion

inferred← citeshttps://doi.org/10.1145/3664647.3680767openalex-snapshot

Informative Point cloud Dataset Extraction for Classification via Gradient-based Points Moving

inferred← citeshttps://doi.org/10.11834/jig.221076openalex-snapshot

Deep learning-based quality enhancement for 3D point clouds：a survey

inferred← citeshttps://doi.org/10.3390/mca31040165openalex-snapshot

A General Transformation Framework from 3-Point Relaxed Binary Subdivision Schemes to 4-Point Relaxed Quaternary Subdivision Schemes for Efficient Curve Modeling

inferred← citeshttps://doi.org/10.3724/sp.j.1089.2021.18817openalex-snapshot

3D Point Cloud Restoration via Deep Learning: A Comprehensive Survey

inferred← citeshttps://doi.org/10.48550/arxiv.2108.07152openalex-snapshot

MSR-GCN: Multi-Scale Residual Graph Convolution Networks for Human Motion Prediction

inferred← citeshttps://doi.org/10.70675/b2c9e492zdb50z460cza375z31b72e195d6fopenalex-snapshot

Étude des croyances addictives, de la régulation émotionnelle et des caractéristiques de personnalité dans l'addiction sexuelle

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