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The centered subdivision mask $[1,2,1]/2$ retains old control points and inserts edge midpoints. Its control polygon is unchanged as a geometric curve by refinement. Its basic limit is the hat $\varphi (t)=\max (1-|t|,0)$, with support $[-1,1]$. This linear spline representation reproduces constants and linear functions, and is $C^0$ but generally not $C^1$ at its spline knots.

If a triangular-lattice subdivision mask has the displayed residue-sum equality, refining data $p_{i,j}=q_j$ gives $p_{I,J}^{\prime }={\sum }_k{b}_{J-3k}{q}_k$, independent of $I$. If affine coordinates reproduce their parameter positions, a geometrically straight extrusion is preserved. Rotationally symmetric masks give the corresponding property in all three lattice-edge directions. A boundary curve compatible with this extrusion uses the collapsed mask $b$, not just the central row of the two-dimensional mask.

Lateral artifacts of a subdivision surface are unwanted variations along an intended straight extrusion: input cross-sections identical along a lattice direction acquire ripples in that direction under refinement. For a stationary arity-$m$ lattice rule $a_{i,j}$, extrusion along the first lattice coordinate is preserved exactly when ${\sum }_{i\equiv r(\mathrm{mod}m)}{a}_{i,j}$ is independent of the residue $r$ for every transverse offset $j$. Then summing along each row gives a single associated univariate subdivision mask. This direction-specific criterion does not promise invariance under every possible oblique extrusion.

Fix an explicit stationary-grid convention. For subdivision arity $m>1$, write the univariate rule asA column of the subdivision matrix is one translated subdivision mask; the next column is translated by $m$ fine-grid rows. Thus the column displacement is the arity. If $a_k$ is active from $k=\alpha$ to $k=\beta$, the subdivision mask width is $w=\beta -\alpha +1$ fine-grid positions, after removing zero padding. Equivalently the mask span is $\beta -\alpha$ fine intervals. This is different from counting old control points in a row stencil: that count measures how many controls influence one newly created point. Stating the convention avoids confusing these two widths.

For a convergent subdivision curve scheme, its support is the influence region of one original control point, equivalently the support of a function of the basic limit $\varphi$ obtained from data $p_j={\delta }_{j0}$. Translation gives the representation $P(t)={\sum }_j{p}_j\varphi (t-j)$. Descendant indices of index zero have parameter positionsThus the limiting support enclosure is $[\alpha /(m-1),\beta /(m-1)]$, as in the support of a stationary subdivision scheme. Endpoint activity gives equality for the ordinary positive convergent masks under discussion.

The functional precision set of a subdivision scheme consists of functions exactly recovered from their uniformly sampled data using the scheme's consistent parameter placement. Polynomial precision records the exactly reproduced polynomial space. Sampling-grid spacing and origin must be specified; the usual arbitrary-grid precision requirement uses every spacing and shift, rather than merely membership in the fixed-grid reconstruction space.

Center the given mask at indices $-1,0,1$. Its binary linear interpolatory subdivision rule isEach new control point is either an old point or an edge midpoint. Hence every refined control polygon is the same curve with extra vertices. Starting from scalar impulse data, the limit is explicitlyThe mask width is three positions, its mask span is two, its largest row stencil uses two old control points, and its subdivision arity is two. Translating the mask indexing to $0,1,2$ translates the basic support to $[0,2]$; the support width remains two original grid intervals.

On $[j,j+1]$, reconstruction is $(j+1-t)p_j+(t-j)p_{j+1}$. Constant and affine samples are therefore recovered exactly. A non-affine polynomial cannot agree with a linear expression on a whole open interval. Thus its polynomial functional precision set of a subdivision scheme isFor continuous functions reproduced at every translated sampling grid, the same conclusion holds: agreement with all chord interpolants forces the affine interpolation identity, hence affinity. At one fixed grid, the larger exactly representable class is the space of linear splines with those spline knots; this is not a higher polynomial precision claim.

For arbitrary data, the one-sided derivatives at $j$ are $p_j-p_{j-1}$ and $p_{j+1}-p_j$. They need not coincide. Therefore the generic limit is $C^0$, not $C^1$. Its first derivative is piecewise constant; exceptional affine data, or individual matching neighboring slopes, can be smoother. This conclusion follows directly from the limit formula, not just from a heuristic mask-width test.

Lateral artifacts of a subdivision surface are spurious changes along an intended straight extrusion. For example, if every row of a control net is the same profile translated along a straight direction, a refinement rule should not create periodic ripples along that direction. This is distinct from changing or smoothing the transverse profile itself.

Use triangular-lattice coordinates $(i,j)$ with basis vectors $e_1=(1,0)$ and $e_2=(1/2,\sqrt{3}/2)$. The displayed mask is supported on $|i|,|j|,|i+j|\le 3$, with the central entry at $(0,0)$. Write its coefficients as $a_{i,j}$, including their division by12. For data constant along horizontal rows, $p_{i,j}=q_j$, ternary refinement givesThus the three residue sums in every mask row must agree if arbitrary row-constant data are to remain row-constant. Equal whole-stencil sums alone only reproduce constants and do not guarantee straight-extrusion preservation.

For the original row $j=1$, the numerator coefficients at $i=-3,-2,-1,0,1,2$ are $2,4,5,5,4,2$. Their three residue sums are $7,8,7$, which do not agree. For $j=2$ the sums are $5,5,4$. In particular input $q_j={\delta }_{j0}$ yields heights $7/12,8/12,7/12$ along the refined row $J=1$. The original mask has lateral artifacts, since it introduces a three-phase ripple into a straight extrusion. The residue-sum criterion is also the usual symbol-factor test: the mask polynomial must contain the ternary averaging factor in the extrusion direction. The original does not. This connects the concrete calculation with the directional artifact analysis in Deriving Box-Spline Subdivision Schemes, while the coefficients and correction here are computed directly from the exam PDF.

A symmetric correction with the same denominator and support is to replace the six nearest-center coefficients5 by6, and the six second-ring corner coefficients3 by2. Leave every other coefficient unchanged, including the central6. The added and subtracted weights balance. For a complete computational specification, the corrected numerator rows areEvery entry in this new table is divided by12. The corresponding horizontal residue sums, before division, areConsequently the corrected rule has extrusion invariance of a ternary triangular scheme. Sixfold symmetry gives the same residue-sum property in the other two lattice-edge directions. Every two-dimensional residue class still sums to12 before normalization, so each refinement stencil sums to one. The weights are nonnegative. Symmetry about the stencil's refined parameter location also reproduces affine coordinates: the class first moments vanish, so an initial straight geometric translation direction remains straight.

The associated univariate boundary subdivision mask is the common sum in each transverse row, rather than the central row of the bivariate mask. Centering it at indices $-3,\dots ,3$ givesIts ternary stencils can be written explicitly asEach stencil sums to one and has the correct affine parameter moment. Applying this rule to a boundary profile agrees with the bivariate rule on a straight extrusion. Endpoint or corner rules must still be chosen for a finite open boundary. The artifact-free conclusion established here concerns the three lattice-edge extrusion directions; it does not promise exact extrusion preservation for every arbitrary off-grid direction. The directional limitation is intrinsic to the mask test, not a reason to substitute the central mask row as a boundary rule.

A norm estimate controls the worst possible refined differences for every choice of the initial controls. Submultiplicativity turns a length-$p$ contraction into a bound for arbitrary long products, so it proves a certain number of continuous derivatives. Its absolute values can lose cancellations; failure of the estimate is inconclusive. It therefore gives a lower bound on smoothness.

Conversely, an observable eigenmode of a repeated local product exhibits a particular sequence of shrinking neighborhoods with inadequate decay. General-position controls have a nonzero coefficient in that mode, so greater derivative continuity is impossible. Special symmetric or polynomial data may suppress the bad mode; this is why the general-position qualification matters. The obstruction gives an upper bound on smoothness, while failure to find an obstruction proves nothing. A lower bound on spectral growth becomes an upper bound on smoothness because large surviving differences correspond to rough behavior.

For example, the binary Cardinal cubic B-spline subdivision mask has symbol $M(z)=(1+z{)}^4/8$, up to an irrelevant index shift. At derivative order $r=2$, the first-difference subdivision mask is $B_2=(1+z)/2$, whose maximum parity sum is $1/2$: the limit is at least $C^2$. At $r=3$, $B_3=1$, and the surviving difference mode does not decay. For generic controls the third derivative is piecewise constant with nonzero jumps, so the subdivision curve is not $C^3$. Here the lower and upper tests determine the exact integer continuity, but they need not coincide for an arbitrary subdivision mask. Stability, sum rules and borderline modes must be handled explicitly before interpreting any numerical estimate as a continuity level.

There are no numerical continuity bounds determined by $w$ and $a$ alone: the subdivision mask coefficients matter. First check constant reproduction, ${\sum }_j{M}_{ϵ-aj}=1$ for every residue $ϵ$, and convergence. For the usual stationary scalar scheme, two useful calculations are a uniform contraction test and a spectral obstruction test. Higher smoothness is tested with derived difference subdivision masks.

Write $M(z)={\sum }_j{M}_j{z}^j$ and $s_a(z)=1+z+\cdots +z^{a-1}$. Using backward differences $\Delta P_i=P_i-P_{i-1}$, the generating-function identityproves the subdivision difference scheme relation $\Delta S_M=S_{M/s_a}\Delta$. Iterating gives ${\Delta }^r{S}_M=S_{M/s_a^r}{\Delta }^r$. To test a candidate $C^r$ limit, suppose the requisite sum rules hold, so $M/s_a^{r+1}$ is a finite Laurent polynomial. The scaled derivative controls $q_i^{\ell }=a^{r\ell }{\Delta }^r{P}_i^{\ell }$ are refined by the subdivision maskwhose residue sums are one. Their first differences are refined byThe powers of $a$ are essential because each refinement shrinks parameter intervals by $a$.

A lower continuity bound comes from proving uniform contraction. The infinity norm of one difference-refinement step is bounded byIf $q<1$, adjacent derivative controls contract geometrically at every location. More generally, form the $p$-step subdivision maskand test ${\max }_{ϵ\mathrm{mod}{a}^p}{\sum }_j|(B_r^{[p]}{)}_{ϵ-a^{p}j}|<1$. This proves convergence of the constant-preserving derivative scheme to a continuous limit. One justification is to compare consecutive piecewise-linear interpolants: their difference is bounded by a constant times the maximum adjacent-control difference, and the geometrically decreasing bound is summable. To identify the limit as $P^{(r)}$, use fixed-degree Cardinal B-spline interpolants of the original refined controls. Their $r$th derivatives are sums of the scaled finite differences; convergence of both functions and derivatives gives $P\in C^r$. Testing successive $r$ supplies a guaranteed lower bound.

An upper continuity bound comes from an observable noncontracting shape mode. Build finite local subdivision matrices for $B_r$ on a refinement-invariant control neighborhood. For a digit $ϵ$ their entries are $(A_{ϵ}{)}_{ij}=(B_r{)}_{ϵ+i-aj}$, since the new index is $ak+ϵ+i$. Restrict to the realizable difference space and discard modes that do not affect the limit. A word ${ϵ}_1,\dots ,{ϵ}_p$ corresponds to a nested parameter location. If its product has an observable eigenvalue $\lambda$ with $|\lambda |>1$, repetition of this word makes some scaled derivative differences grow rather than tend to zero. For a stable limit representation, $C^r$ regularity would force these differences to vanish; hence general-position data cannot have $C^r$ continuity. A modulus-one mode requires examination of whether it actually persists, rather than an automatic rounding rule.

The two calculations can be organized through the joint spectral radius $\hat{\rho }$ of these restricted matrices:These norm and spectral bounds for subdivision regularity make the test computable from the subdivision mask. The right bound certifies all-word contraction when it is less than one; the left bound detects a repeated-word obstruction when it exceeds one. Longer products improve the tests. A single matrix's spectrum is not a substitute for controlling arbitrary products. The product estimates also appear in Charina's analysis of subdivision regularity; the strict contraction and observable-mode arguments above explain their respective roles.

Fix the indexing conventionwhere the subdivision arity $a>1$ is an integer. A change of the initial control $P_0^0$ can influence only indices $i=j_1$ after one step, where $M_{j_1}\ne 0$. After $\ell$ steps the possible descendant indices areLet $j_{\min },j_{\max }$ be the smallest and largest active subdivision mask indices. Dividing by $a^{\ell }$ gives the enclosureTherefore the support of a stationary subdivision scheme, in initial control-grid units, is enclosed byFor the stated symmetric subdivision mask with active extremes $-w,w$, the width is $2w/(a-1)$. For a nonzero compactly supported basic limit function $\varphi$, this is an exact endpoint calculation: its refinement equation is $\varphi (x)={\sum }_j{M}_j\varphi (ax-j)$. If $\beta$ is its right support endpoint, only the largest active subdivision mask index can contribute sufficiently close to the rightmost point of this sum; all smaller indices have argument beyond $\beta$. Hence $\beta =(\beta +j_{\max })/a$, giving $\beta =j_{\max }/(a-1)$. The left endpoint follows in the same way. This argument also excludes cancellation at an extreme for signed subdivision masks. The support for initial control $P_k$ is translated by $k$. If the nominal subdivision mask was padded by zero coefficients, use its actual extremes; and a limiting subdivision curve has to exist before its support is meaningful. This calculation describes the entire possible influence region, rather than the width of a single refinement stencil on its finer grid.

A useful subdivision curve interrogation supplies the following connected capabilities. It describes the parameter interval, open or closed topology, endpoints and break points; it evaluates the limit point $C(t)$ and, where the scheme guarantees them, $C^{\prime }(t)$ and $C^{\prime \prime }(t)$; and it refines or restricts an interval while retaining the mapping to the original parameter. It also provides enclosing bounds and a certified curve-to-chord error on each restricted piece. These are enough to build clipping, intersections, closest-point searches, tangents, curvature and adaptive sampling.

The enquiries concern the subdivision limit, not just a finite refined control polygon. For a stationary local refinement matrix $A$, a limit vertex can be obtained from a normalized left eigenvector $w^{T}A=w^T$, $w^{T}1=1$, as $w^{T}P$. Derivatives use the appropriately scaled difference schemes or equivalent spline evaluation on regular intervals. The convergence and derivative hypotheses must actually hold for the scheme; no second derivative can be requested at an intrinsic crease.

If the subdivision mask is nonnegative and preserves constants, descendants and limit points lie in the relevant control-point convex hull, giving a useful enclosing volume. For subdivision masks with negative weights, that hull need not be an enclosure: the interface must supply another valid bound, for example from a proven refinement-error estimate. This distinction is important for safe spatial rejection in an algorithm.

A subdivision curve should support subdivision curve interrogation rather than merely supply successively larger control polygons. Its interface should provide the parameter interval, orientation and open/closed status; endpoint and limit-point evaluation to a prescribed accuracy; and tangent vector or derivative evaluation where defined. It should be possible to split a parameter interval, commonly at its midpoint, into equivalent restricted curve definitions while retaining all neighboring controls needed by the refinement rule.

For geometric algorithms, request a certified bounding volume for each restricted curve, such as a convex hull or an axis-aligned bounding box, and the minimum and maximum of a plane's linear function over that bound. Request also a certified flatness bound: the maximum distance of the restricted limit curve from its endpoint chord, together with its spatial extent. These allow safe rejection, adaptive drawing and accuracy control. A positive, constant-reproducing subdivision mask often makes a control-point convex hull a valid bound. With negative mask coefficients this must be proved separately or replaced by a padded bound; it cannot simply be assumed.

Further useful enquiries include roots of a scalar function along the curve, closest-point searches, arc length, and inversion of an arc-length parametrization. They can be implemented through evaluation, differentiation, certified bounds and recursive subdivision. Evaluation, restriction, enclosure and error bounds are the essential primitives needed for reliable clipping and rendering; an unqualified control-polygon approximation is insufficient.

The printed diagram is a propagation subdivision mask: it shows the influence of one old vertex on several new vertices. Its entries do not all belong to a single averaging stencil. The old vertex is retained, and a new vertex is inserted at each incident face centre. Reading the contributions arriving at a target givesThus each new vertex-vertex averages its old value and its four edge neighbors, while each new face-vertex is the centroid of its four old corners. Both stencils are convex combinations, reproduce constants and commute with affine maps. The neighboring $1/8$ contributions go to old vertices; the diagonal $2/8$ contributions go to face centres. The propagation coefficients sum to two because one refinement doubles the number of lattice vertices, not because either stencil fails to sum to one.

For calculations, describe this quincunx subdivision usingThe refined square lattice is rotated by $45^{\circ }$ and its spacing is reduced by $\sqrt{2}$. In its integer coordinates, the one-step mask isIndeed, a unit step in these new coordinates is an old face-centre displacement, whereas a diagonal step reaches an old edge neighbor. The even parity class contains the central and four diagonal coefficients and sums to one; the odd class contains the four axial coefficients and also sums to one. This checks the two target stencils independently.

Write the Laurent generating function of the one-step subdivision mask asFor quincunx subdivision, $p_j^{(1)}={\sum }_i{a}_{j-Di}{p}_i$. A second step gives $p_k^{(2)}={\sum }_{i,j}{a}_{k-Dj}{a}_{j-Di}{p}_i$. Setting $r=j-Di$ and $s=k-Dj$, its fine-grid offset is $k-2i=s+Dr$, since $D^2=2I$. Hence the binary mask is the convolution of $a$ with its $D$-transformed copy:Expanding gives the centered binary propagation subdivision mask, with row and column offsets running from $-3$ to $3$:The numerator entries sum to $256$, so $B(1,1)=4$, appropriate for a binary surface refinement. More specifically, each of the four parity classes sums to one after division by $64$, checking constant reproduction at every type of new lattice point.

The factorization of the binary subdivision mask identifies eight box spline directions. Apart from the monomial that centers the support, it isThe four normalized factors correspond respectively to averaging in the directions $(1,0),(0,1),(1,1),(1,-1)$, each with multiplicity two. The factor four is the determinant of the binary dilation in two dimensions. The centering monomial translates the resulting box spline and changes neither its polynomial degree nor its smoothness. Thus this is the twice-smoothed four-direction box spline, rather than a tensor-product bicubic B-spline.

Treat the subdivision surface as an evaluable limit together with its refinement hierarchy. At a requested location $\xi$, evaluate the limit position $S(\xi )$ and two independent limit tangent vectors, then setOn regular regions, local spline patches supply position and derivative evaluations. Near extraordinary vertices, use the actual subdivision rule and its limit-position and tangent evaluation machinery, or a convergent local refinement with controlled position and normal vector errors. If a tangent pair degenerates, the regular offset evaluator is not defined there; a crease requires separate one-sided offsets and an explicitly chosen join.

For polygonal interrogation, subdivide the base mesh adaptively. At every new sample, evaluate the base limit and its limit normal vector, and displace that sample by $dn$. Connect these samples with the inherited connectivity, refining until flatness and normal-variation bounds meet the desired tolerance. If the position error is $\delta$ and unit-normal error is $\eta$, the offset-position error is at most $\delta +|d|\eta$, in addition to the interpolation error between samples. A variable normal offset can be sampled at the same locations, with its approximation error included as well. The result retains the base subdivision hierarchy for locating points and refining patches.

Displacing the original control vertices along estimated vertex normals and applying the original subdivision mask just once does not, in general, give the exact surface offset. If $A$ is one linear refinement step, then $A(P+dn(P))=AP+dAn(P)$, while displacing the refined base gives $AP+dn(AP)$; normalization and the normal calculation are nonlinear, so $An(P)\ne n(AP)$ in general. Thus the exact offset is an interrogation wrapper around the base limit, and a conventional subdivision control mesh for it is a fitted approximation requiring refinement or error control. Refine and interrogate the base limit, then offset its evaluated points; do not assume offsetting commutes with subdivision.

The transverse row sequence in the extrusion invariance of Loop subdivision obeysIn the convention $g_k^{\prime }={\sum }_j{a}_{k-2j}{g}_j$, the centered univariate subdivision mask isThe even coefficients sum to one and the odd coefficients sum to one; the whole binary mask sums to two. Its generating expression is $a(z)=z^{-2}(1+z{)}^4/8$, identifying the Cardinal cubic B-spline refinement rule. Its limit is $G(y)={\sum }_j{g}_j{M}_3(y-j)$ for the centered unit-grid cubic basis $M_3$. The curve across rows is this cubic B-spline; extrusion generates the whole limit surface. The along-extrusion direction itself has constant height, so its profile is a straight line rather than an additional nonconstant cubic curve.

The supplied subdivision mask describes the regular, valence-six triangular grid. Its coefficients split into four parity classes after binary refinement: one class for old vertices and three classes for the three edge directions. For an old vertex $V$ with cyclic neighbors $V_1,\dots ,V_6$, the even stencil isFor an edge with endpoints $A,B$ and opposite vertices $C,D$ in its two incident triangles, the new odd vertex isRotating this edge stencil supplies the other two odd parity classes. All stencil weights sum to one, giving affine invariance; their first moments place an affine regular grid at the old positions and edge midpoints. Apply the same weights separately to $x,y,z$. Boundary edges and extraordinary vertices need additional rules, which are not specified by this uniform mask. The subsequent arguments concern the regular interior grid and compatible extruded boundary treatment.

Interpret polynomial reproduction as a guarantee for every polynomial of degree at most $d$, with the planar grid coordinates fixed by the affine-exact refinement. Constants are unchanged because every stencil sums to one. Linear height functions are unchanged because each stencil's weighted position is its refined grid location. Therefore the Loop subdivision limit reproduces all affine polynomials exactly.

Quadratic data already disprove the next degree. Use extruded data $g_j=j^2$ on a unit-spaced transverse row coordinate $y$. At spacing $h$, both even and odd rules from the induced subdivision mask give the value of $y^2$ at the refined location plus $h^2/4$. For the even stencil this follows fromand for the odd stencil from the average of the values at $y-h/2$ and $y+h/2$. Constants are reproduced, so the accumulated vertical displacement over all refinement levels isThe limit is $y^2+1/3$, not the original quadratic graph. HenceThe word “every” matters: some special higher-degree bivariate polynomials have cancelling second moments and can be reproduced. The conclusion concerns the entire polynomial space, which already fails at degree two.

A regular quincunx subdivision step replaces a square lattice by the union of its old vertices and face centres. In lattice indices one may useThus two steps give ordinary binary refinement. If $A(z,w)$ is the one-step subdivision mask symbol, the two-step symbol is $A(z,w)A(zw,z/w)$. The two lattice parity classes separately determine the old-vertex and face-vertex stencils; their coefficient sums must each be one to reproduce constants.

A subdivision curve is the limit of repeated refinement of a control polygon, when that limit exists. Each finite stage still consists of line segments; the limiting curve can have a continuous derivative. Linear stationary rules, which use the same fixed weighted averages at every stage, can be studied through their subdivision masks and often represent splines. An interpolation rule retains the old control points, as in binary linear interpolatory subdivision; an approximating rule such as Chaikin subdivision generally moves away from them. Increasing the number of control points alone does not prove convergence or smoothness.

// Target: analysis.bigb

A useful subdivision curve interface provides parameter ranges, endpoint and limit evaluation, derivatives, subdivision into equivalent restricted pieces, a certified bounding volume, and a certified error relative to a chord. Positive subdivision masks often give a convex hull bound. Masks with negative coefficients need a separate enclosing bound; their control-point hull need not enclose the limit curve.