collada-dae-parser

collada-dae-parser

Parse collada .dae file vertex positions, textures, normals and animations

View live demo

WIP

This package is a work in progress so check back later.

Will be adding an api, cli, and example gh-pages

To Install

$ npm install --save collada-dae-parser

CLI

Output stringified JSON to stdout

# parse from stdin
cat my-3d-model.dae | dae2json > parsed-model.json

# parse from file
dae2json my-3d-modal.dae > parsed-model.json

API

parseDae(xmlString, callback) -> object

xmlString

Required

Type: string

Your collada file as a string

callback

Required

Type: function

function (err, parsedDaeObject) {
  console.log(parsedDaeObject)
  /*
  {
    bindShapeMatrix: [...],
    keyframes: {...},
    vertexNormalIndices: [...],
    vertexNormals: [...],
    vertexPositionIndices: [...],
    vertexPositions: [...],
    vertexUVIndices: [...],
    vertexUVs: [...]
  }
  */
}

TODO:

• basic cli (potentially pull into own repo, but start here)

• rounding values. Currently lots of .999999 and 1.000001

• fix normals in demo lighting

• add a textured complex demo model

• allow zoom in, zoom out in demo

• Factor in bind shape matrix

• Stop exporting bind shape matrix

• toggle animation on and off

• multiple animations in demo

• full screen demo with controls overlayed

• All of the TODO: statements in code

• Allow file buffer to be passed in

• Add a GIF of every test fixture animation

• Documentation / references

• We might want another module to export the raw useful data.. Then we can transform it / add opinions higher up.

References

• waZim. (2010) "Step by Step Skeletal Animation in C++ and OpenGL, Using COLLADA Part 1 & Part 2"
  • Heavily informed the initial parser
• Markus Ruh. (2012) Vertex Skinning
  • Heavily informed the vertex skinning in the initial demo vertex shader
• Jerimiah van Oosten. (2011) "GPU Skinning of MD5 Models in OpenGL and Cg"
  • Heavily informed the vertex skinning in the initial demo vertex shader

See Also

• wavefront-obj-parser

License

MIT