Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jun 7, 2026

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

Learning External Point-Set Context for Point Cloud Segmentation.

Jibin Peng, Haotian Dong, Xin Wang

IEEE Transactions on Neural Networks and Learning Systems

|June 5, 2026

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Autophagy modulation in gynaecologic oncology: insights into immune regulation and therapeutic potential.

Frontiers in immunology·2026

Same author

Holmium vanadate nanoparticles as a biosafe and efficient contrast agent for spectral CT imaging of gastritis and colitis.

Regenerative biomaterials·2026

Same author

Elucidating the metabolic dynamics and aroma formation mechanisms of sun-exposed versus rain-soaked fresh tea leaves during Wuyi rock tea processing via GC-MS and LC-MS-based untargeted metabolomics.

Food chemistry·2026

Same author

Global prevalence of pan-vascular diseases: A trend and health inequality analyses.

Cardiovascular research·2026

Same author

DDARes-U<sup>2</sup>Net: a dual-decoder adversarial residual U<sup>2</sup>Net algorithm for segmentation of COVID-19 pneumonia lesions.

Journal of medical imaging (Bellingham, Wash.)·2026

Same author

Bypassing Schottky constraints via defect-mediated hydrogen transfer in hydrodechlorination.

Nature communications·2026

Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026

Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

IEEE transactions on neural networks and learning systems·2026

Same journal

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

IEEE transactions on neural networks and learning systems·2026

Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026

Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026

Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026

See all related articles

This study introduces external point-set context (EPSC) for 3D point cloud semantic segmentation. EPSC leverages external memory to enhance context, significantly improving segmentation accuracy across multiple datasets.

Area of Science:

Computer Vision
Machine Learning
3D Data Analysis

Background:

Point cloud semantic segmentation relies heavily on visual context to understand 3D point relationships.
Current methods primarily use internal context from within the same object or scene.
A need exists for richer contextual information to improve segmentation performance.

Purpose of the Study:

To introduce and evaluate a novel approach using external point-set context (EPSC) for 3D point cloud semantic segmentation.
To enhance the understanding of semantic relationships between 3D points by incorporating information from diverse objects and scenes.
To improve the accuracy and robustness of point cloud segmentation.

Main Methods:

Proposed External Point-Set Context (EPSC) method utilizing an external memory system.

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Related Experiment Videos

Last Updated: Jun 7, 2026

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

External memory stores cluster features representing relationships between adjacent 3D points.

EPSC representations are learned during training and released during inference to provide contextual cues.

Main Results:

Demonstrated effective improvement in point cloud semantic segmentation.
Achieved significant performance gains on benchmark datasets: Stanford Large-Scale 3-D Indoor Spaces (S3DIS), ScanNetv2, and ShapeNetPart.
The proposed EPSC method provides rich and relevant context for accurate segmentation.

Conclusions:

External point-set context (EPSC) is a valuable addition to 3D point cloud semantic segmentation.
The external memory approach effectively captures and utilizes cross-object/scene contextual information.
The method shows strong potential for advancing the field of 3D semantic understanding.