fn get_normal_basic(p: vec3<f32>, obj_params: vec4<f32>) -> vec3<f32> {
    let obj_type = obj_params.x;
    if (obj_type == 1.0) { return normalize(p); }
    let e = vec2<f32>(0.001, 0.0);
    return normalize(vec3<f32>(
        get_dist(p + e.xyy, obj_params) - get_dist(p - e.xyy, obj_params),
        get_dist(p + e.yxy, obj_params) - get_dist(p - e.yxy, obj_params),
        get_dist(p + e.yyx, obj_params) - get_dist(p - e.yyx, obj_params)
    ));
}

// Optimized normal estimation using tetrahedron pattern (4 SDF evals instead of 6).
// Slightly less accurate than central differences but faster.
// Uses tetrahedral gradient approximation with corners at (±1, ±1, ±1).
fn get_normal_fast(p: vec3<f32>, obj_params: vec4<f32>) -> vec3<f32> {
    let obj_type = obj_params.x;
    if (obj_type == 1.0) { return normalize(p); }
    let eps = 0.0001;
    let k = vec2<f32>(1.0, -1.0);
    return normalize(
        k.xyy * get_dist(p + k.xyy * eps, obj_params) +
        k.yyx * get_dist(p + k.yyx * eps, obj_params) +
        k.yxy * get_dist(p + k.yxy * eps, obj_params) +
        k.xxx * get_dist(p + k.xxx * eps, obj_params)
    );
}

// Distance to an Axis-Aligned Bounding Box
fn aabb_sdf(p: vec3<f32>, min_p: vec3<f32>, max_p: vec3<f32>) -> f32 {
    let center = (min_p + max_p) * 0.5;
    let extent = (max_p - min_p) * 0.5;
    let q = abs(p - center) - extent;
    return length(max(q, vec3<f32>(0.0))) + min(max(q.x, max(q.y, q.z)), 0.0);
}