Tsuyoshi Kishigami

dblp:263/7970 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Geometric modeling and processing · 67% Computational photography and imaging · 33%
Theoretical computer science
1 paper
Mathematical optimization · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Geometric modeling and processing
3d reconstruction
0.612022
ISHIGAKI Retrieval System Using 3D Shape Matching and Combinatorial Optimization · Int. J. Comput. Vis. 2022
Computational photography and imaging › image-based modeling
3d reconstruction from images
0.612022
ISHIGAKI Retrieval System Using 3D Shape Matching and Combinatorial Optimization · Int. J. Comput. Vis. 2022
Geometric modeling and processing
shape matching
0.612022
ISHIGAKI Retrieval System Using 3D Shape Matching and Combinatorial Optimization · Int. J. Comput. Vis. 2022
Mathematical optimization › combinatorial optimization
assignment problem
0.612022
ISHIGAKI Retrieval System Using 3D Shape Matching and Combinatorial Optimization · Int. J. Comput. Vis. 2022
Mathematical optimization
combinatorial optimization
0.612022
ISHIGAKI Retrieval System Using 3D Shape Matching and Combinatorial Optimization · Int. J. Comput. Vis. 2022

Methods — techniques the papers use, named apart from their topics

iterative closest point · 1.1assignment algorithm · 1.1
YearPublicationVenuePosition
2022 ISHIGAKI Retrieval System Using 3D Shape Matching and Combinatorial Optimization
abstract
Abstract In April 2016, a massive earthquake with a magnitude of 7.3 struck Kumamoto region, Japan, causing major devastation. One of the structures that were damaged in Kumamoto was Kumamoto Castle, a cultural asset of great significance in Japan. The stone retaining wall “ishigaki” that formed the foundation of the castle collapsed, and the superstructure was destroyed. The number of stones is estimated to be more than 70,000, and restoration work is anticipated to take more than 20 years. Since each of the stones is an important cultural asset, the broken stone structure needed to be restored to its original state in order not to lose its cultural value forever. In addition, the fallen stones need to be returned to their original positions in the ishigakis. In similar cases, non-automatic visual verification was used. However, for Kumamoto Castle, this would have been impossible because a large number of stones were displaced as a result of the collapse. The purpose of this project is to provide support for the restoration work by matching the stones that fell down after the collapse with those before the collapse using information technology, such as computer vision and optimization technologies. Specifically, we captured photographic images of the stones before and after the collapse to match them. The technical contributions of this study are as follows: (a) To estimate the scale and surface orientation of the stones, we exploit 3D model construction from the images. (b) To solve the jigsaw-puzzle-like problem of reassembling the stone fragments, we exploit the combination of a customized iterative closest point (ICP) algorithm for shape position matching and an assignment algorithm to find the best pairs of stones before and after the collapse by using the matching degree obtained from ICP. Here, only the 2D shape of the stones before the collapse can be used due to the small number of photos available. In contrast, a detailed 3D shape can be obtained from the stones after their collapse. We matched these asymmetric data in 2D and 3D to enable a comprehensive reconstruction. (c) We developed a user-friendly graphical user interface system that was used by actual masons without special knowledge. The developed system was used to match the ishigaki of a turret, Iidamaru. As a result, we succeeded in identifying 337 stones, or approximately 90% of the 370 images. These results are expected to be useful for and were used as a blueprint during actual restoration work.
Gou Koutaki, Sakino Ando, Keiichiro Shirai, Tsuyoshi Kishigami
Int. J. Comput. Vis.4