triangles-distance-fast

signed or unsigned distance to triangles mesh using rtree and/or bvh tree

uses bvh-tree-plus for the bvh tree and rbush-3d for the rtree.

we use the rtree to quickly get a list of triangles potentially inside a bounding box, and the bvh tree to determine if a point is inside/outside the mesh via raycasting.

npm i triangles-distance-fast

//triangle = [[x,y,z],[x,y,z],[x,y,z]]
//aabb = [[x,y,z],[x,y,z]] //axis aligned bounding box, [min, max]

//newest, no need for dist limit
// trianglesDistanceKnn(pt, triangles, nNeighbors=10, trisRTree)

//older ones 
// trianglesDistance(tris, radius, rtree?) // unsigned distance function(x,y,z) sampling over radius, use optional cached rtree - returns >9999 if no triangles within radius
// trianglesDistance_signed(tris, radius, rtree?, bvh?) // signed distance function(x,y,z) sampling over radius, use optional cached rtree and bvh tree - returns abs() > 9999 if no triangles within radius
// trianglesDistanceStable(pt, triangles, trisRTree, eps=0.1, S=1000) unsigned, sampling over 3 narrow bounding boxes along the XYZ axis 
// trianglesDistanceBruteForce(pt, triangles) //brute force / naive approach, testing each triangle
// trianglesIntersectingAABB(tris, aabb, rtree?) // get set of triangles whose bounding boxes intersect the aabb
// triangles2RTree(tris) //get rtree for triangles 
// triangles2BvhTree(tris) //get bvh for triangles 
// trianglesFlatten(tris) //flatten tris into 1-d float32 array

// funcs using a kd-tree based on loading triangle verts [note that searching using the vertices of the triangles rather than 'bounding volumes' may introduce edge cases that the r-tree does not]
// trianglesDistance_KdTree(tris, maxDist=Infinity, existingTree) //unsigned dist using a kd tree
// triangles2KdTree(tris) //convert tris to static-kdtree

// stochastic kd tree generates random pts on the surface of the mesh rather than just using the triangle verts 
// trianglesDistance_KdTree_stochastic(tris, radius=Infinity, _existingStochasticTreeDescriptor=null, nPtsSampler=tris.length*5)
// triangles2KdTreeStochastic(tris, nPtsToSample, skipAddVerts = false) //result may be less 'biased' if we dont add the triangle verts themselves

var tdf = require('triangles-distance-fast'); 
var fs = require("fs");
var stl = require('stl');
var path = require('path');

//load tris from stl file
var triangles = stl.toObject(fs.readFileSync(path.join(__dirname,'Bitey_Reconstructed_5k.stl'))).facets.map(function(f){return f.verts});

//make distance function
var df = tdf.trianglesDistance(triangles,20.0);

//use it
var dist = df(10,10,10);
console.log(dist); //5.114792068071368

//returns >9999 if nothing found within the radius [or < -9999 if using signed distance inside mesh]