Active learning with joint probabilistic modeling for point cloud semantic segmentation

Baochen Yao; Dongjie Zhang; Jie Zhao; Ye Zheng; Chengbin Peng

doi:10.1016/j.knosys.2025.114171

ScienceGate Book Chapters

JOURNAL ARTICLE

Active learning with joint probabilistic modeling for point cloud semantic segmentation

Baochen Yao Dongjie Zhang Jie Zhao Ye Zheng Chengbin Peng

Year: 2025 Journal: Knowledge-Based Systems Vol: 328 Pages: 114171-114171 Publisher: Elsevier BV

DOI: 10.1016/j.knosys.2025.114171

Get Full-Text PDF Get Analytical Report

Keywords:

Segmentation Computer science Point cloud Joint (building) Probabilistic logic Artificial intelligence Point (geometry) Machine learning Mathematics Engineering

Metrics

Cited By

0.00

FWCI (Field Weighted Citation Impact)

Refs

0.31

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Topics

3D Surveying and Cultural Heritage

Physical Sciences → Earth and Planetary Sciences → Geology

Remote Sensing and LiDAR Applications

Physical Sciences → Environmental Science → Environmental Engineering

Image Processing and 3D Reconstruction

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Active learning with joint probabilistic modeling for point cloud semantic segmentation

Metrics

Topics

Related Documents

EFFICIENT TRAINING OF SEMANTIC POINT CLOUD SEGMENTATION VIA ACTIVE LEARNING

Active and incremental learning for semantic ALS point cloud segmentation

Hierarchical Point-based Active Learning for Semi-supervised Point Cloud Semantic Segmentation

Label-Efficient Point Cloud Semantic Segmentation: A Holistic Active Learning Approach

A multi-granularity semisupervised active learning for point cloud semantic segmentation