COLLADA: sailing the gulf of 3D digital content creation by Remi Arnaud, Mark C. Barnes

By Remi Arnaud, Mark C. Barnes

COLLADA is a COLLAborative layout task for developing an open ordinary electronic Asset schema for interactive 3D purposes. This publication explains intimately how one can use the COLLADA know-how in a undertaking using 3D resources, and finally the best way to create a good content material production pipeline for the main complicated improvement. Errata are published at http://collada.org/mediawiki/index.php/COLLADA_book.

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These elements are used as data-stream building blocks within the element. Arrays The vast majority of data is represented by arrays of values, without semantics. In the original design, array data was weakly typed and required extra metadata in the instance document to describe the contents fully. Unfortunately, XML Schema–aware parsing tools could not validate this data because the schema did not contain enough information. The designers considered this a problem because the array elements contained the bulk of the data in the document.

3 COLLADA Geometry Overview This chapter explains how to assemble geometry in COLLADA. The data model for COLLADA geometry is a stream-processing model [38] in which data sources are assembled into data sinks. In COLLADA, the data sinks are elements, and the data sources are, oddly enough,elements. A coherent data set is contained within a element. Assembling the data streams into a geometry mesh involves several elements, including , , ,, , and .

In that case, the XML format can be two to three times larger than the binary data. 40 Chapter 2: COLLADA Document On the other hand, some numbers are much smaller to encode in text than in IEEE binary encoding. For instance, all the numbers from 0 to 9 can be encoded in two bytes (one for the number, one for the space), and yet they still require four bytes when encoded in binary. Measurement on real data and experimentation is the best way to compare the two approaches. It turns out that the size of the data is very similar in either case.

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