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#include <pybind11/pybind11.h>
#include <pybind11/numpy.h>
#include <pybind11/eigen.h>
#include <cpu/fill_holes.h>
#include <cpu/merge_vertices.h>
// CPU sparse marching cubes
#include <cpu/hashtable.h>
#include <cpu/spmc.h>
// CPU mesh decimator
#include <cpu/decimation.h>
#include <vector>
#include <cstring>
namespace py = pybind11;
namespace cubvh {
static py::array_t<int> fill_holes(
py::array_t<float, py::array::c_style | py::array::forcecast> vertices,
py::array_t<int, py::array::c_style | py::array::forcecast> faces,
bool return_added,
bool check_containment,
double eps_d,
bool verbose) {
// Validate shapes
auto vbuf = vertices.request();
auto fbuf = faces.request();
if (!(vbuf.ndim == 2 && vbuf.shape[1] == 3)) {
throw std::runtime_error("vertices must be of shape [N,3]");
}
if (!(fbuf.ndim == 2 && fbuf.shape[1] == 3)) {
throw std::runtime_error("faces must be of shape [M,3]");
}
const size_t N = static_cast<size_t>(vbuf.shape[0]);
const size_t M = static_cast<size_t>(fbuf.shape[0]);
const float* vptr = static_cast<const float*>(vbuf.ptr);
const int* fptr = static_cast<const int*>(fbuf.ptr);
std::vector<Eigen::Vector3f> V(N);
for (size_t i = 0; i < N; ++i) {
V[i] = Eigen::Vector3f(vptr[3*i+0], vptr[3*i+1], vptr[3*i+2]);
}
std::vector<Eigen::Vector3i> F(M);
for (size_t i = 0; i < M; ++i) {
F[i] = Eigen::Vector3i(fptr[3*i+0], fptr[3*i+1], fptr[3*i+2]);
}
cubvh::cpu::HoleFillOptions opt;
opt.checkContainment = check_containment;
opt.eps = static_cast<float>(eps_d);
opt.verbose = verbose;
if (return_added) {
auto added = cubvh::cpu::fill_holes(V, F, opt);
py::array_t<int> out({(py::ssize_t)added.size(), (py::ssize_t)3});
auto obuf = out.request();
int* optr = static_cast<int*>(obuf.ptr);
for (size_t i = 0; i < added.size(); ++i) {
optr[3*i+0] = added[i][0];
optr[3*i+1] = added[i][1];
optr[3*i+2] = added[i][2];
}
return out;
} else {
cubvh::cpu::fill_holes_inplace(V, F, opt);
py::array_t<int> out({(py::ssize_t)F.size(), (py::ssize_t)3});
auto obuf = out.request();
int* optr = static_cast<int*>(obuf.ptr);
for (size_t i = 0; i < F.size(); ++i) {
optr[3*i+0] = F[i][0];
optr[3*i+1] = F[i][1];
optr[3*i+2] = F[i][2];
}
return out;
}
}
// merge_vertices binding: returns (vertices, faces) after merge
static std::pair<py::array_t<float>, py::array_t<int>> merge_vertices(
py::array_t<float, py::array::c_style | py::array::forcecast> vertices,
py::array_t<int, py::array::c_style | py::array::forcecast> faces,
double threshold_d) {
auto vbuf = vertices.request();
auto fbuf = faces.request();
if (!(vbuf.ndim == 2 && vbuf.shape[1] == 3)) {
throw std::runtime_error("vertices must be of shape [N,3]");
}
if (!(fbuf.ndim == 2 && fbuf.shape[1] == 3)) {
throw std::runtime_error("faces must be of shape [M,3]");
}
const size_t N = static_cast<size_t>(vbuf.shape[0]);
const size_t M = static_cast<size_t>(fbuf.shape[0]);
const float* vptr = static_cast<const float*>(vbuf.ptr);
const int* fptr = static_cast<const int*>(fbuf.ptr);
std::vector<Eigen::Vector3f> V(N);
for (size_t i=0;i<N;++i) {
V[i] = Eigen::Vector3f(vptr[3*i+0], vptr[3*i+1], vptr[3*i+2]);
}
std::vector<Eigen::Vector3i> F(M);
for (size_t i=0;i<M;++i) {
F[i] = Eigen::Vector3i(fptr[3*i+0], fptr[3*i+1], fptr[3*i+2]);
}
std::vector<Eigen::Vector3f> V_out; std::vector<Eigen::Vector3i> F_out;
cubvh::cpu::merge_vertices(V, F, static_cast<float>(threshold_d), V_out, F_out);
py::array_t<float> v_out({(py::ssize_t)V_out.size(), (py::ssize_t)3});
py::array_t<int> f_out({(py::ssize_t)F_out.size(), (py::ssize_t)3});
auto vObuf = v_out.request(); auto fObuf = f_out.request();
float* vO = static_cast<float*>(vObuf.ptr);
int* fO = static_cast<int*>(fObuf.ptr);
for (size_t i=0;i<V_out.size();++i) {
vO[3*i+0] = V_out[i][0]; vO[3*i+1] = V_out[i][1]; vO[3*i+2] = V_out[i][2];
}
for (size_t i=0;i<F_out.size();++i) {
fO[3*i+0] = F_out[i][0]; fO[3*i+1] = F_out[i][1]; fO[3*i+2] = F_out[i][2];
}
return {v_out, f_out};
}
// sparse marching cubes (CPU): returns (vertices [M,3] float32, faces [T,3] int32)
static std::pair<py::array_t<float>, py::array_t<int>> sparse_marching_cubes_cpu(
py::array_t<int, py::array::c_style | py::array::forcecast> coords,
py::array_t<float, py::array::c_style | py::array::forcecast> corners,
double iso_d,
bool ensure_consistency = false) {
auto cbuf = coords.request();
auto vbuf = corners.request();
if (!(cbuf.ndim == 2 && cbuf.shape[1] == 3)) {
throw std::runtime_error("coords must be of shape [N,3] (int32)");
}
if (!(vbuf.ndim == 2 && vbuf.shape[1] == 8)) {
throw std::runtime_error("corners must be of shape [N,8] (float32)");
}
if (cbuf.shape[0] != vbuf.shape[0]) {
throw std::runtime_error("coords and corners must have the same first dimension N");
}
const int N = static_cast<int>(cbuf.shape[0]);
const int* cptr = static_cast<const int*>(cbuf.ptr);
const float* fptr = static_cast<const float*>(vbuf.ptr);
const float iso = static_cast<float>(iso_d);
auto mesh = cubvh::cpu::sparse_marching_cubes(cptr, fptr, N, iso, ensure_consistency);
const auto& V = mesh.first;
const auto& F = mesh.second;
// Allocate outputs
py::array_t<float> v_out({(py::ssize_t)V.size(), (py::ssize_t)3});
py::array_t<int> f_out({(py::ssize_t)F.size(), (py::ssize_t)3});
auto vObuf = v_out.request();
auto fObuf = f_out.request();
float* vO = static_cast<float*>(vObuf.ptr);
int* fO = static_cast<int*>(fObuf.ptr);
for (size_t i = 0; i < V.size(); ++i) {
vO[3*i+0] = V[i].x;
vO[3*i+1] = V[i].y;
vO[3*i+2] = V[i].z;
}
for (size_t i = 0; i < F.size(); ++i) {
fO[3*i+0] = F[i].v0;
fO[3*i+1] = F[i].v1;
fO[3*i+2] = F[i].v2;
}
return {v_out, f_out};
}
// CPU decimator bindings
template <typename T>
static cubvh::cpu::qd::MeshT<T> _mesh_from_numpy_typed(
py::array_t<T, py::array::c_style | py::array::forcecast> vertices,
py::array_t<int, py::array::c_style | py::array::forcecast> faces)
{
if (vertices.ndim() != 2 || vertices.shape(1) != 3)
throw std::runtime_error("vertices must be (N,3) array");
if (faces.ndim() != 2 || faces.shape(1) != 3)
throw std::runtime_error("faces must be (M,3) int32 array");
cubvh::cpu::qd::MeshT<T> m;
m.vertices.reserve(vertices.shape(0));
auto vbuf = vertices.template unchecked<2>();
for (ssize_t i = 0; i < vertices.shape(0); ++i) {
m.vertices.emplace_back(vbuf(i,0), vbuf(i,1), vbuf(i,2));
}
m.faces.reserve(faces.shape(0));
auto fbuf = faces.template unchecked<2>();
for (ssize_t i = 0; i < faces.shape(0); ++i) {
m.faces.push_back({ fbuf(i,0), fbuf(i,1), fbuf(i,2) });
}
return m;
}
template <typename T>
static std::pair<py::array_t<T>, py::array_t<int>> _mesh_to_numpy_typed(const cubvh::cpu::qd::MeshT<T>& m)
{
py::array_t<T> V({ (ssize_t)m.vertices.size(), (ssize_t)3 });
py::array_t<int> F({ (ssize_t)m.faces.size(), (ssize_t)3 });
auto vbuf = V.template mutable_unchecked<2>();
for (ssize_t i = 0; i < (ssize_t)m.vertices.size(); ++i) {
vbuf(i,0) = m.vertices[i].x;
vbuf(i,1) = m.vertices[i].y;
vbuf(i,2) = m.vertices[i].z;
}
auto fbuf = F.template mutable_unchecked<2>();
for (ssize_t i = 0; i < (ssize_t)m.faces.size(); ++i) {
fbuf(i,0) = m.faces[i][0];
fbuf(i,1) = m.faces[i][1];
fbuf(i,2) = m.faces[i][2];
}
return { V, F };
}
static py::tuple decimate(py::array vertices,
py::array faces,
int target_vertices)
{
py::dtype dt = vertices.dtype();
if (dt.is(py::dtype::of<float>())){
auto v = vertices.cast<py::array_t<float, py::array::c_style | py::array::forcecast>>();
auto f = faces.cast<py::array_t<int, py::array::c_style | py::array::forcecast>>();
auto mesh = _mesh_from_numpy_typed<float>(v, f);
cubvh::cpu::qd::DecimatorT<float> dec(mesh);
dec.decimate(target_vertices);
auto out = _mesh_to_numpy_typed<float>(dec.mesh());
return py::make_tuple(out.first, out.second);
} else if (dt.is(py::dtype::of<double>())){
auto v = vertices.cast<py::array_t<double, py::array::c_style | py::array::forcecast>>();
auto f = faces.cast<py::array_t<int, py::array::c_style | py::array::forcecast>>();
auto mesh = _mesh_from_numpy_typed<double>(v, f);
cubvh::cpu::qd::DecimatorT<double> dec(mesh);
dec.decimate(target_vertices);
auto out = _mesh_to_numpy_typed<double>(dec.mesh());
return py::make_tuple(out.first, out.second);
} else {
throw std::runtime_error("vertices must be float32 or float64 array");
}
}
static py::tuple parallel_decimate(py::array vertices,
py::array faces,
int target_vertices)
{
py::dtype dt = vertices.dtype();
if (dt.is(py::dtype::of<float>())){
auto v = vertices.cast<py::array_t<float, py::array::c_style | py::array::forcecast>>();
auto f = faces.cast<py::array_t<int, py::array::c_style | py::array::forcecast>>();
auto mesh = _mesh_from_numpy_typed<float>(v, f);
cubvh::cpu::qd::DecimatorT<float> dec(mesh);
dec.parallelDecimate(target_vertices);
auto out = _mesh_to_numpy_typed<float>(dec.mesh());
return py::make_tuple(out.first, out.second);
} else if (dt.is(py::dtype::of<double>())){
auto v = vertices.cast<py::array_t<double, py::array::c_style | py::array::forcecast>>();
auto f = faces.cast<py::array_t<int, py::array::c_style | py::array::forcecast>>();
auto mesh = _mesh_from_numpy_typed<double>(v, f);
cubvh::cpu::qd::DecimatorT<double> dec(mesh);
dec.parallelDecimate(target_vertices);
auto out = _mesh_to_numpy_typed<double>(dec.mesh());
return py::make_tuple(out.first, out.second);
} else {
throw std::runtime_error("vertices must be float32 or float64 array");
}
}
// CPU Hash Table bindings
class HashTable {
public:
HashTable() {}
void set_num_dims(int d) { ht.set_num_dims(d); }
int get_num_dims() const { return ht.get_num_dims(); }
void resize(int capacity) { ht.resize(capacity); }
void prepare() { ht.prepare(); }
void insert(at::Tensor coords) {
TORCH_CHECK(!coords.is_cuda(), "coords must reside on CPU");
TORCH_CHECK(coords.dtype() == at::kInt, "coords must be int32");
TORCH_CHECK(coords.dim() == 2, "coords must be 2D [N,D]");
coords_ref_ = coords.contiguous();
const int N = (int)coords_ref_.size(0);
const int D = (int)coords_ref_.size(1);
ht.set_num_dims(D);
ht.insert(coords_ref_.data_ptr<int>(), N);
}
void build(at::Tensor coords) {
TORCH_CHECK(!coords.is_cuda(), "coords must reside on CPU");
TORCH_CHECK(coords.dtype() == at::kInt, "coords must be int32");
TORCH_CHECK(coords.dim() == 2, "coords must be 2D [N,D]");
coords_ref_ = coords.contiguous();
const int N = (int)coords_ref_.size(0);
const int D = (int)coords_ref_.size(1);
ht.set_num_dims(D);
ht.build(coords_ref_.data_ptr<int>(), N);
}
at::Tensor search(at::Tensor queries) const {
TORCH_CHECK(!queries.is_cuda(), "queries must reside on CPU");
TORCH_CHECK(queries.dtype() == at::kInt, "queries must be int32");
TORCH_CHECK(queries.dim() == 2, "queries must be 2D [M,D]");
TORCH_CHECK(ht.capacity > 0, "hash table is not built");
at::Tensor q = queries.contiguous();
const int M = (int)q.size(0);
auto opts_i = torch::TensorOptions().dtype(torch::kInt32).device(q.device());
at::Tensor out = at::empty({M}, opts_i);
ht.search(q.data_ptr<int>(), M, out.data_ptr<int>());
return out;
}
private:
mutable HashTableIntCPU ht;
at::Tensor coords_ref_;
};
} // namespace cubvh |