Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 197

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Registration and integration of multiple range images for 3-D model construction

Masuda, T., Sakaue, K., Yokoya, N.·1996·Proceedings of 13th International Conference on Pattern Recognition

Abstract

Registration and integration of measured data of real objects are becoming important in 3D modeling for computer graphics and computer-aided design. We propose a new algorithm of registration and integration of multiple range images for producing a geometric object surface model. The registration algorithm determines a set of rigid motion parameters that register a range image to a given mesh-based geometric model. The algorithm is an integration of the iterative closest point algorithm with the least median of squares estimator. After registration, points in the input range are classified into inliers and outliers according to registration errors between each data point and the model. The outliers are appended to the surface model to be used by registration with the following range images. The parts classified as inlier by at least one registration result is segmented out to be integrated. This process consisting of registration and integration is iterated until all views are integrated. We successfully experimented with the proposed method on real range image sequences taken by a rangefinder. The method does not need any preliminary processes.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/icpr.1996.546150

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 1 references

Cited by

See all 197

Metadata

Authors
Masuda, T., Sakaue, K., & Yokoya, N.
Venue
Proceedings of 13th International Conference on Pattern Recognition
Publisher
IEEE
Published
1996
Pages
879-883 vol.1
ISSN
1041-3278 (ISSN-L)
OpenAlex
W2011549544
Cited by
197
References
1
FWCI
6.39
Type
conference paper
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Registration and integration of multiple range images for 3-D model construction. https://app.cochat.ai/papers/10.1109/icpr.1996.546150
The paper
Masuda, T., Sakaue, K., & Yokoya, N. (1996). Registration and integration of multiple range images for 3-D model construction. Proceedings of 13th International Conference on Pattern Recognition. https://doi.org/10.1109/icpr.1996.546150

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.