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Intelligent Reverse-Engineering Segmentation: Automatic Semantic Recognition of Large 3D Digitalized Cloud of Points Dedicated to Heritage Objects

Abstract : In this article we present a multidisciplinary experimentation realized between a mechanical laboratory, a computer scientist laboratory and a museum. Our goal is to provide automatic tools for non-expert people who want to use 3D digitized elements. After scanning an objet, we obtain a huge amount of points. In order to manipulate it, it is necessary to decimate it. However, when doing this operation, we can optimize the algorithms for creating semantic topology; obviously we can do it automatically. Consequently, we are going to do what we name segmentation: we extract meaning from 3D points and meshes. Our experimentation deals with a physical mock-up of Nantes city that have been designed in 1900. After digitalization, we have created a software that can: 1. use the whole 3D cloud of points as an input; 2. fill a knowledge database with an intelligent segmentation of the 3D virtual models: ground, walls, roofs... This use case is the first step of our research. At the end, we aim to deploy our method to complex mechanical parts. Nowadays, when designing CAD parts we use as well as volume parts than surface parts or meshes. We know is it not necessary to reconstruct all the triangles. It is a lost of time and we can directly use cloud of points for CAD design. However, the design tree will not be updated. So, with our method, imagine that one day we can digitalize a motor and a system could automatically create the 3D mock-up and the design tree.
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Florent Laroche, Daniel Lefevre, Myriam Servières, Benjamin Hervy, Alain Bernard. Intelligent Reverse-Engineering Segmentation: Automatic Semantic Recognition of Large 3D Digitalized Cloud of Points Dedicated to Heritage Objects. ASME 2012 11th Biennial Conference on Engineering Systems Design and Analysis, Jul 2012, Nantes, France. ⟨10.1115/ESDA2012-82824⟩. ⟨hal-00817295⟩

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