Make Shapes and Signals Meet: A Gentle Guide to BCPD and DET

Make Shapes and Signals Meet is an illustrated, beginner-friendly guide to BCPD and DET. BCPD aligns shapes using point locations, while DET also uses signal information—such as image intensities, audio amplitudes, or gene-expression features—to guide registration. The guide introduces the algorithms and related methods, including BCPD++, GBCPD, and DLD. It provides practical instructions for installation, running Python demonstrations, visualizing and saving results, choosing parameters and kernels, using hierarchical registration, and reducing computation time and memory. It also explains output files and demonstrates how to assess registration quality. Examples cover point clouds, anatomical meshes, images, audio signals, and spatial transcriptomics, including MERFISH and MOSTA. A two-page Quick Reference summarizes common commands, parameters, and outputs. This edition is Manual v0.99 and describes software v0.99. The software and links to demonstration data are available at https://github.com/ohirose/bcpd. Original text and illustrations are licensed under CC BY 4.0, except where otherwise indicated. Third-party material retains its original rights and applicable terms; see the captions and credits.

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-16
DOI
https://doi.org/10.5281/zenodo.23406851
Primary Topic
3D Shape Modeling and Analysis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Make Shapes and Signals Meet: A Gentle Guide to BCPD and DET

Osamu Hirose
Zenodo (CERN European Organization for Nuclear Research)
3D Shape Modeling and Analysis
article

Make Shapes and Signals Meet: A Gentle Guide to BCPD and DET

Osamu Hirose
article en

Abstract

Make Shapes and Signals Meet is an illustrated, beginner-friendly guide to BCPD and DET. BCPD aligns shapes using point locations, while DET also uses signal information—such as image intensities, audio amplitudes, or gene-expression features—to guide registration. The guide introduces the algorithms and related methods, including BCPD++, GBCPD, and DLD. It provides practical instructions for installation, running Python demonstrations, visualizing and saving results, choosing parameters and kernels, using hierarchical registration, and reducing computation time and memory. It also explains output files and demonstrates how to assess registration quality. Examples cover point clouds, anatomical meshes, images, audio signals, and spatial transcriptomics, including MERFISH and MOSTA. A two-page Quick Reference summarizes common commands, parameters, and outputs. This edition is Manual v0.99 and describes software v0.99. The software and links to demonstration data are available at https://github.com/ohirose/bcpd. Original text and illustrations are licensed under CC BY 4.0, except where otherwise indicated. Third-party material retains its original rights and applicable terms; see the captions and credits.

Zenodo (CERN European Organization for Nuclear Research)
Kanazawa University (JP)
Openalex Percentile: Top 19%
3D Shape Modeling and Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.