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warp.mesh_query_point_sign_normal#

warp.mesh_query_point_sign_normal(
id: uint64,
point: vec3f,
max_dist: float32,
epsilon: float32,
) → MeshQueryPoint#

  • Kernel

  • Differentiable

Compute the closest point on the warp.Mesh with identifier id to the given point in space.

Identifies the sign of the distance (inside/outside) using the angle-weighted pseudo normal. This approach to sign determination is robust for well conditioned meshes that are watertight and non-self intersecting. It is also comparatively fast to compute.

Triangles that are degenerate or nearly degenerate relative to their edge lengths are excluded from the closest-point search, so such a face can be skipped even when it satisfies the distance constraint. If every face satisfying the distance constraint is excluded, the query returns no result.

Parameters:

  • id – The mesh identifier

  • point – The query point, in the mesh’s local space

  • max_dist – Maximum allowed distance to the returned closest point. The query returns no result if no face is strictly closer than this distance.

  • epsilon – Epsilon treating distance values as equal, when locating the minimum distance vertex/face/edge, as a fraction of the average edge length, also for treating closest point as being on edge/vertex default 1e-3.

Returns:

A warp.MeshQueryPoint. Check result first (True if a face within max_dist was found), then read sign (< 0 if point is inside the mesh, >= 0 if outside), face (index of the closest face), and the barycentric coordinates u and v of the closest point on that face. Pass face, u and v to mesh_eval_position() to obtain the closest point’s position.

Example

@wp.kernel
def classify(mesh_id: wp.uint64, p: wp.vec3, out_inside: wp.array[wp.int32]):
    res = wp.mesh_query_point_sign_normal(mesh_id, p, 1.0e6)
    if res.result:
        out_inside[0] = wp.where(res.sign < 0.0, 1, 0)

points = wp.array([[0,0,0],[1,0,0],[1,1,0],[0,1,0],[0,0,1],[1,0,1],[1,1,1],[0,1,1]], dtype=wp.vec3)
indices = wp.array([0,3,2, 0,2,1,  4,5,6, 4,6,7,  0,1,5, 0,5,4,
                    2,3,7, 2,7,6,  0,4,7, 0,7,3,  1,2,6, 1,6,5], dtype=wp.int32)
mesh = wp.Mesh(points=points, indices=indices)

out_inside = wp.zeros(1, dtype=wp.int32)
wp.launch(classify, dim=1, inputs=[mesh.id, wp.vec3(0.5, 0.5, 0.5)], outputs=[out_inside])
print("inside:", bool(out_inside.numpy()[0]))

inside: True