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warp.mesh_query_aabb_tiled#

warp.mesh_query_aabb_tiled(
id: uint64,
low: vec3f,
high: vec3f,
) → MeshQueryAABBTiled#

  • Kernel

Construct an axis-aligned bounding box (AABB) query against a warp.Mesh for thread-block parallel traversal.

For use in tiled kernels: all threads in the block cooperatively traverse the mesh’s BVH. Advance the query with mesh_query_aabb_next_tiled() (one face index per thread per step) in a loop guarded by tile_query_valid(). low and high must be identical across all threads in the block and are given in the mesh’s local space.

Parameters:

  • id – The mesh identifier

  • low – The lower bound of the query box, in the mesh’s local space (must be the same for all threads in the block)

  • high – The upper bound of the query box, in the mesh’s local space (must be the same for all threads in the block)

Returns:

A warp.MeshQueryAABBTiled to advance with mesh_query_aabb_next_tiled().

Example

@wp.kernel
def tiled_faces(mesh_id: wp.uint64, lo: wp.vec3, hi: wp.vec3, out_count: wp.array[wp.int32]):
    query = wp.mesh_query_aabb_tiled(mesh_id, lo, hi)
    while wp.tile_query_valid(query):
        result = wp.mesh_query_aabb_next_tiled(query)
        face = wp.untile(result)
        if face >= 0:
            wp.atomic_add(out_count, 0, 1)

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_count = wp.zeros(1, dtype=wp.int32)
wp.launch_tiled(tiled_faces, dim=[1], inputs=[mesh.id, wp.vec3(-1.0, -1.0, -1.0), wp.vec3(2.0, 2.0, 2.0)], outputs=[out_count], block_dim=32)
print("overlapping faces:", out_count.numpy()[0])

overlapping faces: 12