/**
 * Sample interior Steiner points for the volume enclosed by a closed
 * triangle surface, on a jittered axis-aligned grid sized by
 * `cells_per_dim`. Points outside the surface are discarded; what remains
 * is a Float32Array of (x, y, z) tuples that lies strictly inside the
 * surface (modulo classification noise on near-surface samples).
 *
 * Intended as input to `compute_delaunay_tetrahedral_mesh` alongside the
 * surface vertices, so the Delaunay tet mesh has interior detail rather
 * than being made entirely of surface-bound tets. Without these Steiner
 * points, every tet of a non-trivial volume straddles or touches the
 * surface, blowing up centroid-classification discretization error in
 * the carving step.
 *
 * Grid cells are scaled so the longest bounding-box axis gets
 * `cells_per_dim` cells; other axes get a proportional share rounded to
 * the nearest integer (minimum 1). Each cell contributes one candidate
 * point at its center, perturbed by a deterministic seeded jitter of up
 * to ±0.35 cell widths — enough to break Delaunay-pathological
 * alignments without losing the grid's spatial uniformity.
 *
 * @param {BinaryTopology} mesh closed surface
 * @param {number} cells_per_dim grid resolution along the longest bbox axis
 * @param {number} [seed] seed for the jitter RNG; same seed → same points
 * @returns {Float32Array} flat (x, y, z) interior point positions
 */
export function bt_mesh_sample_interior_grid_points(mesh: BinaryTopology, cells_per_dim: number, seed?: number): Float32Array;
//# sourceMappingURL=bt_mesh_sample_interior_grid_points.d.ts.map