How to check whether the point is in the tetrahedron or not?

Question

I know all coordinates of tetrahedron and the point I would like to determine. So does anyone know how to do it? I've tried to determine the point's belonging to each triangle of tetrahedron, and if it's true to all triangles then the point is in the tetrahedron. But it's absolutely wrong.

Answer 1

For each plane of the tetrahedron, check if the point is on the same side as the remaining vertex:

bool SameSide(v1, v2, v3, v4, p)
{
    normal := cross(v2 - v1, v3 - v1)
    dotV4 := dot(normal, v4 - v1)
    dotP := dot(normal, p - v1)
    return Math.Sign(dotV4) == Math.Sign(dotP);
}

And you need to check this for each plane:

bool PointInTetrahedron(v1, v2, v3, v4, p)
{
    return SameSide(v1, v2, v3, v4, p) &&
           SameSide(v2, v3, v4, v1, p) &&
           SameSide(v3, v4, v1, v2, p) &&
           SameSide(v4, v1, v2, v3, p);               
}

Answer 2

You define a tetrahedron by four vertices, ABC and D. Therefore you also can have the 4 triangles defining the surface of the tetrahedron.

You now just check if a point P is on the other side of the plane. The normal of each plane is pointing away from the center of the tetrahedron. So you just have to test against 4 planes.

Your plane equation looks like this: a*x+b*y+c*z+d=0 Just fill in the point values (xyz). If the sign of the result is >0 the point is of the same side as the normal, result == 0, point lies in the plane, and in your case you want the third option: <0 means it is on the backside of the plane. If this is fulfilled for all 4 planes, your point lies inside the tetrahedron.

Answer 3

Given 4 points A,B,C,D defining a non-degenerate tetrahedron, and a point P to test, one way would be to transform the coordinates of P into the tetrahedron coordinate system, for example taking A as the origin, and the vectors BA, CA, DA as the unit vectors.

In this coordinate system, the coordinates of P are all between 0 and 1 if it is inside P, but it could also be in anywhere in the transformed cube defined by the origin and the 3 unit vectors. One way to assert that P is inside (A,B,C,D) is by taking in turn as origin the points (A, B, C, and D) and the other three points to define a new coordinate system. This test repeated 4 times is effective but can be improved.

It is most efficient to transform the coordinates only once and reuse the SameSide function as proposed earlier, for example taking A as the origin, transforming into the (A,B,C,D) coordinates system, P and A must lie on the same side of the (B,C,D) plane.

Following is a numpy/python implementation of that test. Tests indicate this method is 2-3 times faster than the Planes method.

import numpy as np

def sameside(v1,v2,v3,v4,p):
    normal = np.cross(v2-v1, v3-v1)
    return ((np.dot(normal, v4-v1)*p.dot(normal, p-v1) > 0)

def tetraCoord(A,B,C,D):
    v1 = B-A ; v2 = C-A ; v3 = D-A
    # mat defines an affine transform from the tetrahedron to the orthogonal system
    mat = np.concatenate((np.array((v1,v2,v3,A)).T, np.array([[0,0,0,1]])))
    # The inverse matrix does the opposite (from orthogonal to tetrahedron)
    M1 = np.linalg.inv(mat)
    return(M1)

def pointInsideT(v1,v2,v3,v4,p):
    # Find the transform matrix from orthogonal to tetrahedron system
    M1=tetraCoord(v1,v2,v3,v4)
    # apply the transform to P
    p1 = np.append(p,1)
    newp = M1.dot(p1)
    # perform test
    return(np.all(newp>=0) and np.all(newp <=1) and sameside(v2,v3,v4,v1,p))

Answer 4

Starting from Hugues' solution , here is a simpler and (even) more efficient one:

import numpy as np

def tetraCoord(A,B,C,D):
  # Almost the same as Hugues' function, 
  # except it does not involve the homogeneous coordinates.
  v1 = B-A ; v2 = C-A ; v3 = D-A
  mat = np.array((v1,v2,v3)).T
  # mat is 3x3 here
  M1 = np.linalg.inv(mat)
  return(M1)

def pointInside(v1,v2,v3,v4,p):
  # Find the transform matrix from orthogonal to tetrahedron system
  M1=tetraCoord(v1,v2,v3,v4)
  # apply the transform to P (v1 is the origin)
  newp = M1.dot(p-v1)
  # perform test
  return (np.all(newp>=0) and np.all(newp <=1) and np.sum(newp)<=1)

In the coordinate system associated to the tetrahedron, the opposite face from the origin (denoted v1 here) is characterized by x+y+z=1. Thus, the half space on the same side as v1 from this face satisfies x+y+z<1.

As a comparison, here is the full code for comparing the methods proposed by Nico , Hugues and me:

import numpy as np
import time

def sameside(v1,v2,v3,v4,p):
    normal = np.cross(v2-v1, v3-v1)
    return (np.dot(normal, v4-v1) * np.dot(normal, p-v1) > 0)

# Nico's solution
def pointInside_Nico(v1,v2,v3,v4,p):   
    return sameside(v1, v2, v3, v4, p) and sameside(v2, v3, v4, v1, p) and sameside(v3, v4, v1, v2, p) and sameside(v4, v1, v2, v3, p)      

# Hugues' solution
def tetraCoord(A,B,C,D):
    v1 = B-A ; v2 = C-A ; v3 = D-A
    # mat defines an affine transform from the tetrahedron to the orthogonal system
    mat = np.concatenate((np.array((v1,v2,v3,A)).T, np.array([[0,0,0,1]])))
    # The inverse matrix does the opposite (from orthogonal to tetrahedron)
    M1 = np.linalg.inv(mat)
    return(M1)
    
def pointInside_Hugues(v1,v2,v3,v4,p):
    # Find the transform matrix from orthogonal to tetrahedron system
    M1=tetraCoord(v1,v2,v3,v4)
    # apply the transform to P
    p1 = np.append(p,1)
    newp = M1.dot(p1)
    # perform test
    return(np.all(newp>=0) and np.all(newp <=1) and sameside(v2,v3,v4,v1,p))    

# Proposed solution
def tetraCoord_Dorian(A,B,C,D):
    v1 = B-A ; v2 = C-A ; v3 = D-A
    # mat defines an affine transform from the tetrahedron to the orthogonal system
    mat = np.array((v1,v2,v3)).T
    # The inverse matrix does the opposite (from orthogonal to tetrahedron)
    M1 = np.linalg.inv(mat)
    return(M1) 

def pointInside_Dorian(v1,v2,v3,v4,p):
    # Find the transform matrix from orthogonal to tetrahedron system
    M1=tetraCoord_Dorian(v1,v2,v3,v4)
    # apply the transform to P
    newp = M1.dot(p-v1)
    # perform test
    return (np.all(newp>=0) and np.all(newp <=1) and np.sum(newp)<=1)
    
npt=100000    
Pt=np.random.rand(npt,3)
A=np.array([0.1, 0.1, 0.1])
B=np.array([0.9, 0.2, 0.1])
C=np.array([0.1, 0.9, 0.2])
D=np.array([0.3, 0.3, 0.9])

inTet_Nico=np.zeros(shape=(npt,1),dtype=bool)
inTet_Hugues=inTet_Nico
inTet_Dorian=inTet_Nico

start_time = time.time()
for i in range(0,npt):
    inTet_Nico[i]=pointInside_Nico(A,B,C,D,Pt[i,:])
print("--- %s seconds ---" % (time.time() - start_time)) # https://stackoverflow.com/questions/1557571/how-do-i-get-time-of-a-python-programs-execution

start_time = time.time()
for i in range(0,npt):
    inTet_Hugues[i]=pointInside_Hugues(A,B,C,D,Pt[i,:])
print("--- %s seconds ---" % (time.time() - start_time))   

start_time = time.time()    
for i in range(0,npt):
    inTet_Dorian[i]=pointInside_Dorian(A,B,C,D,Pt[i,:])
print("--- %s seconds ---" % (time.time() - start_time)) 

And here are the results, in terms of running time:

--- 15.621951341629028 seconds ---
--- 8.97989797592163 seconds ---
--- 4.597853660583496 seconds ---

---

[EDIT]

Based on the Tom's idea of vectorizing the process, if one wants to find which element of a mesh contains a given point, here is a somehow highly vectorized solution:

Input data:

• node_coordinates : ( n_nodes ,3) array containing the coordinates of each node
• node_ids : ( n_tet , 4) array, where the i -th row gives the vertex indices of the i -th tetrahedron.

def where(node_coordinates, node_ids, p):
    ori=node_coordinates[node_ids[:,0],:]
    v1=node_coordinates[node_ids[:,1],:]-ori
    v2=node_coordinates[node_ids[:,2],:]-ori
    v3=node_coordinates[node_ids[:,3],:]-ori
    n_tet=len(node_ids)
    v1r=v1.T.reshape((3,1,n_tet))
    v2r=v2.T.reshape((3,1,n_tet))
    v3r=v3.T.reshape((3,1,n_tet))
    mat = np.concatenate((v1r,v2r,v3r), axis=1)
    inv_mat = np.linalg.inv(mat.T).T    # https://stackoverflow.com/a/41851137/12056867        
    if p.size==3:
        p=p.reshape((1,3))
    n_p=p.shape[0]
    orir=np.repeat(ori[:,:,np.newaxis], n_p, axis=2)
    newp=np.einsum('imk,kmj->kij',inv_mat,p.T-orir)
    val=np.all(newp>=0, axis=1) & np.all(newp <=1, axis=1) & (np.sum(newp, axis=1)<=1)
    id_tet, id_p = np.nonzero(val)
    res = -np.ones(n_p, dtype=id_tet.dtype) # Sentinel value
    res[id_p]=id_tet
    return res

The hack here is to do the matrix product with multidimensional arrays.

The where function takes the point(s) coordinates as the 3rd argument. Indeed, this function can be run on multiple coordinates at once; the output argument is of same length as p . It returns -1 if the corresponding coordinate is not in the mesh.

On a mesh consisting in 1235 tetrahedrons, this method is 170-180 times faster than looping over each tetrahedron. Such a mesh is very small, so this gap may increase for larger meshes.

Answer 5

I've vectorized Dorian and Hughes solutions to take the entire array of points as input. I also moved the tetraCoord function outside of the pointsInside function and renamed both, since there was no point in calling it for every point.

On my computer, @Dorian's solution and example runs in 2.5 seconds. On the same data, mine runs nearly a thousand times faster at 0.003 seconds. If one for some reason needs even more speed, importing the GPU cupy package as "np" pushes it into the 100 microsecond range.

import time
# alternatively, import cupy as np if len(points)>1e7 and GPU
import numpy as np 

def Tetrahedron(vertices):
    """
    Given a list of the xyz coordinates of the vertices of a tetrahedron, 
    return tetrahedron coordinate system
    """
    origin, *rest = vertices
    mat = (np.array(rest) - origin).T
    tetra = np.linalg.inv(mat)
    return tetra, origin

def pointInside(point, tetra, origin):
    """
    Takes a single point or array of points, as well as tetra and origin objects returned by 
    the Tetrahedron function.
    Returns a boolean or boolean array indicating whether the point is inside the tetrahedron.
    """
    newp = np.matmul(tetra, (point-origin).T).T
    return np.all(newp>=0, axis=-1) & np.all(newp <=1, axis=-1) & (np.sum(newp, axis=-1) <=1)

npt=10000000
points = np.random.rand(npt,3)
# Coordinates of vertices A, B, C and D
A=np.array([0.1, 0.1, 0.1])
B=np.array([0.9, 0.2, 0.1])
C=np.array([0.1, 0.9, 0.2])
D=np.array([0.3, 0.3, 0.9])

start_time = time.time()
vertices = [A, B, C, D]
tetra, origin = Tetrahedron(vertices)
inTet = pointInside(points, tetra, origin)
print("--- %s seconds ---" % (time.time() - start_time)) 

Answer 6

Thanks to Dorian's test case script i could work on yet another solution and compare it quickly to the ones so far.

the intuition

for a triangle ABC and a point P if one connects P to the corners to get the vectors PA, PB, PC and compares the two triangles X and Y that are spanned by PA,PC and PB,PC, then the point P lies within the triangle ABC if X and Y overlap.

Or in other words, it is impossible to construct the vector PA by linearly combining PC and PB with only positive coefficients if P is in the triangle ABC.

From there on i tried to transfer it to the tetrahedron case and read here that it is possible to check whether vectors are linearly independent by checking the determinant of the matrix constructed out of the vectors as columns to be non-zero. I tried out various approaches using determinants and i stumbled across this one :

Let PA, PB, PC, PD be the connections of P to the tetrahedron points ABCD (ie PA = A - P, etc.). calculate the determinants detA = det(PB PC PD), detB, detC and detD (like detA).

Then the point P lies within the tetrahedron spanned by ABCD if:

detA > 0 and detB < 0 and detC > 0 and detD < 0

or

detA < 0 and detB > 0 and detC < 0 and detD > 0

so the determinants switch signs, starting from negative, or starting from positive.

Does it work ? Apparently. Why does it work ? I don't know, or at least, i can't proof it. Maybe someone else with better math skills can help us here.

(EDIT: actually barycentric coordinates can be defined using those determinants and in the end, the barycentric coordinates need to sum up to one. It is like comparing the volumes of the tetrahedra that are spanned by the combinations of P with the points A,B,C,D with the volume of the tetrahedron ABCD itself. The explained case with observing the determinant signs is still unclear whether it works in general and i don't recommend it)

i changed the test case to not check n points Pi against one tetrahedron T, but to check n points Pi against n tetrahedrons Ti. All of the answers still give correct results. I think the reason why this approach is faster is that it doesn't need a matrix inversion. I left TomNorway's approach implemented with one tetrahedron, and i leave the vectorization of this new approach to others since i am not so familiar with python and numpy.

import numpy as np
import time

def sameside(v1,v2,v3,v4,p):
    normal = np.cross(v2-v1, v3-v1)
    return (np.dot(normal, v4-v1) * np.dot(normal, p-v1) > 0)

# Nico's solution
def pointInside_Nico(v1,v2,v3,v4,p):   
    return sameside(v1, v2, v3, v4, p) and sameside(v2, v3, v4, v1, p) and sameside(v3, v4, v1, v2, p) and sameside(v4, v1, v2, v3, p)      

# Hugues' solution
def tetraCoord(A,B,C,D):
    v1 = B-A ; v2 = C-A ; v3 = D-A
    # mat defines an affine transform from the tetrahedron to the orthogonal system
    mat = np.concatenate((np.array((v1,v2,v3,A)).T, np.array([[0,0,0,1]])))
    # The inverse matrix does the opposite (from orthogonal to tetrahedron)
    M1 = np.linalg.inv(mat)
    return(M1)

def pointInside_Hugues(v1,v2,v3,v4,p):
    # Find the transform matrix from orthogonal to tetrahedron system
    M1=tetraCoord(v1,v2,v3,v4)
    # apply the transform to P
    p1 = np.append(p,1)
    newp = M1.dot(p1)
    # perform test
    return(np.all(newp>=0) and np.all(newp <=1) and sameside(v2,v3,v4,v1,p))    

#Dorian's solution
def tetraCoord_Dorian(A,B,C,D):
    v1 = B-A ; v2 = C-A ; v3 = D-A
    # mat defines an affine transform from the tetrahedron to the orthogonal system
    mat = np.array((v1,v2,v3)).T
    # The inverse matrix does the opposite (from orthogonal to tetrahedron)
    M1 = np.linalg.inv(mat)
    return(M1) 

def pointInside_Dorian(v1,v2,v3,v4,p):
    # Find the transform matrix from orthogonal to tetrahedron system
    M1=tetraCoord_Dorian(v1,v2,v3,v4)
    # apply the transform to P
    newp = M1.dot(p-v1)
    # perform test
    return (np.all(newp>=0) and np.all(newp <=1) and np.sum(newp)<=1)

#TomNorway's solution adapted to cope with n tetrahedrons

def Tetrahedron(vertices):
    """
    Given a list of the xyz coordinates of the vertices of a tetrahedron, 
    return tetrahedron coordinate system
    """
    origin, *rest = vertices
    mat = (np.array(rest) - origin).T
    tetra = np.linalg.inv(mat)
    return tetra, origin

def pointInside(point, tetra, origin):
    """
    Takes a single point or array of points, as well as tetra and origin objects returned by 
    the Tetrahedron function.
    Returns a boolean or boolean array indicating whether the point is inside the tetrahedron.
    """
    newp = np.matmul(tetra, (point-origin).T).T
    return np.all(newp>=0, axis=-1) & np.all(newp <=1, axis=-1) & (np.sum(newp, axis=-1) <=1)



# Proposed solution
def det3x3_Philipp(b,c,d):
    return b[0]*c[1]*d[2] + c[0]*d[1]*b[2] + d[0]*b[1]*c[2] - d[0]*c[1]*b[2] - c[0]*b[1]*d[2] - b[0]*d[1]*c[2]

def pointInside_Philipp(v0,v1,v2,v3,p):
    a = v0 - p
    b = v1 - p
    c = v2 - p
    d = v3 - p
    detA = det3x3_Philipp(b,c,d)
    detB = det3x3_Philipp(a,c,d)
    detC = det3x3_Philipp(a,b,d)
    detD = det3x3_Philipp(a,b,c)
    ret0 = detA > 0.0 and detB < 0.0 and detC > 0.0 and detD < 0.0
    ret1 = detA < 0.0 and detB > 0.0 and detC < 0.0 and detD > 0.0
    return ret0 or ret1


npt=100000
Pt= np.array([ np.array([p[0]-0.5,p[1]-0.5,p[2]-0.5]) for p in np.random.rand(npt,3)])
A=np.array([ np.array([p[0]-0.5,p[1]-0.5,p[2]-0.5]) for p in np.random.rand(npt,3)])
B=np.array([ np.array([p[0]-0.5,p[1]-0.5,p[2]-0.5]) for p in np.random.rand(npt,3)])
C=np.array([ np.array([p[0]-0.5,p[1]-0.5,p[2]-0.5]) for p in np.random.rand(npt,3)])
D=np.array([ np.array([p[0]-0.5,p[1]-0.5,p[2]-0.5]) for p in np.random.rand(npt,3)])

inTet_Nico=np.zeros(shape=(npt,1),dtype=bool)
inTet_Hugues=np.copy(inTet_Nico)
inTet_Dorian=np.copy(inTet_Nico)
inTet_Philipp=np.copy(inTet_Nico)



print("non vectorized, n points, different tetrahedrons:")

start_time = time.time()
for i in range(0,npt):
    inTet_Nico[i]=pointInside_Nico(A[i,:],B[i,:],C[i,:],D[i,:],Pt[i,:])
print("Nico's:   --- %s seconds ---" % (time.time() - start_time)) # https://stackoverflow.com/questions/1557571/how-do-i-get-time-of-a-python-programs-execution

start_time = time.time()
for i in range(0,npt):
    inTet_Hugues[i]=pointInside_Hugues(A[i,:],B[i,:],C[i,:],D[i,:],Pt[i,:])
print("Hugues':  --- %s seconds ---" % (time.time() - start_time))   

start_time = time.time()    
for i in range(0,npt):
    inTet_Dorian[i]=pointInside_Dorian(A[i,:],B[i,:],C[i,:],D[i,:],Pt[i,:])
print("Dorian's: --- %s seconds ---" % (time.time() - start_time))

start_time = time.time()
for i in range(0,npt):
    inTet_Philipp[i]=pointInside_Philipp(A[i,:],B[i,:],C[i,:],D[i,:],Pt[i,:])
print("Philipp's:--- %s seconds ---" % (time.time() - start_time))   

print("vectorized, n points, 1 tetrahedron:")

start_time = time.time()
vertices = [A[0], B[0], C[0], D[0]]
tetra, origin = Tetrahedron(vertices)
inTet_Tom = pointInside(Pt, tetra, origin)
print("TomNorway's: --- %s seconds ---" % (time.time() - start_time)) 


for i in range(0,npt):
    assert inTet_Hugues[i] == inTet_Nico[i]
    assert inTet_Dorian[i] == inTet_Hugues[i]
    #assert inTet_Tom[i] == inTet_Dorian[i] can not compare because Tom implements 1 tetra instead of n
    assert inTet_Philipp[i] == inTet_Dorian[i]

'''errors = 0
for i in range(0,npt):
    if ( inTet_Philipp[i] != inTet_Dorian[i]):
        errors = errors + 1 
print("errors " + str(errors))'''

Results:

non vectorized, n points, different tetrahedrons:
Nico's:   --- 25.439453125 seconds ---
Hugues':  --- 28.724457263946533 seconds ---
Dorian's: --- 15.006574153900146 seconds ---
Philipp's:--- 4.389788389205933 seconds ---
vectorized, n points, 1 tetrahedron:
TomNorway's: --- 0.008165121078491211 seconds ---