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nanite.cpp
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// This is a playground for experimenting with algorithms necessary for Nanite like hierarchical clustering
// The code is not optimized, not robust, and not intended for production use.
// It optionally supports METIS for clustering and partitioning, with an eventual goal of removing this code
// in favor of meshopt algorithms.
// For reference, see the original Nanite paper:
// Brian Karis. Nanite: A Deep Dive. 2021
#ifndef _CRT_SECURE_NO_WARNINGS
#define _CRT_SECURE_NO_WARNINGS
#endif
#include "../src/meshoptimizer.h"
#include <float.h>
#include <math.h>
#include <stdio.h>
#include <string.h>
#include <algorithm>
#include <map> // only for METIS
#include <vector>
#ifndef _WIN32
#include <dlfcn.h>
#endif
#define METIS_OK 1
#define METIS_OPTION_SEED 8
#define METIS_OPTION_UFACTOR 16
#define METIS_NOPTIONS 40
static int METIS = 0;
static int (*METIS_SetDefaultOptions)(int* options);
static int (*METIS_PartGraphRecursive)(int* nvtxs, int* ncon, int* xadj,
int* adjncy, int* vwgt, int* vsize, int* adjwgt,
int* nparts, float* tpwgts, float* ubvec, int* options,
int* edgecut, int* part);
#ifndef TRACE
#define TRACE 0
#endif
struct Vertex
{
float px, py, pz;
float nx, ny, nz;
float tx, ty;
};
struct LODBounds
{
float center[3];
float radius;
float error;
};
struct Cluster
{
std::vector<unsigned int> indices;
LODBounds self;
LODBounds parent;
};
const size_t kClusterSize = 128;
const size_t kGroupSize = 8;
const bool kUseLocks = true;
const bool kUseNormals = true;
const bool kUseRetry = true;
const int kMetisSlop = 2;
const float kSimplifyThreshold = 0.85f;
static LODBounds bounds(const std::vector<Vertex>& vertices, const std::vector<unsigned int>& indices, float error)
{
meshopt_Bounds bounds = meshopt_computeClusterBounds(&indices[0], indices.size(), &vertices[0].px, vertices.size(), sizeof(Vertex));
LODBounds result;
result.center[0] = bounds.center[0];
result.center[1] = bounds.center[1];
result.center[2] = bounds.center[2];
result.radius = bounds.radius;
result.error = error;
return result;
}
static LODBounds boundsMerge(const std::vector<Cluster>& clusters, const std::vector<int>& group)
{
std::vector<LODBounds> bounds(group.size());
for (size_t j = 0; j < group.size(); ++j)
bounds[j] = clusters[group[j]].self;
meshopt_Bounds merged = meshopt_computeSphereBounds(&bounds[0].center[0], bounds.size(), sizeof(LODBounds), &bounds[0].radius, sizeof(LODBounds));
LODBounds result = {};
result.center[0] = merged.center[0];
result.center[1] = merged.center[1];
result.center[2] = merged.center[2];
result.radius = merged.radius;
// merged bounds error must be conservative wrt cluster errors
result.error = 0.f;
for (size_t j = 0; j < group.size(); ++j)
result.error = std::max(result.error, clusters[group[j]].self.error);
return result;
}
// computes approximate (perspective) projection error of a cluster in screen space (0..1; multiply by screen height to get pixels)
// camera_proj is projection[1][1], or cot(fovy/2); camera_znear is *positive* near plane distance
// for DAG cut to be valid, boundsError must be monotonic: it must return a larger error for parent cluster
// for simplicity, we ignore perspective distortion and use rotationally invariant projection size estimation
static float boundsError(const LODBounds& bounds, float camera_x, float camera_y, float camera_z, float camera_proj, float camera_znear)
{
float dx = bounds.center[0] - camera_x, dy = bounds.center[1] - camera_y, dz = bounds.center[2] - camera_z;
float d = sqrtf(dx * dx + dy * dy + dz * dz) - bounds.radius;
return bounds.error / (d > camera_znear ? d : camera_znear) * (camera_proj * 0.5f);
}
static std::vector<Cluster> clusterizeMetis(const std::vector<Vertex>& vertices, const std::vector<unsigned int>& indices);
static std::vector<std::vector<int> > partitionMetis(const std::vector<Cluster>& clusters, const std::vector<int>& pending, const std::vector<unsigned int>& remap);
static std::vector<Cluster> clusterize(const std::vector<Vertex>& vertices, const std::vector<unsigned int>& indices)
{
if (METIS & 2)
return clusterizeMetis(vertices, indices);
const size_t max_vertices = 192; // TODO: depends on kClusterSize, also may want to dial down for mesh shaders
const size_t max_triangles = kClusterSize;
const size_t min_triangles = (kClusterSize / 3) & ~3;
const float split_factor = 2.0f;
size_t max_meshlets = meshopt_buildMeshletsBound(indices.size(), max_vertices, min_triangles);
std::vector<meshopt_Meshlet> meshlets(max_meshlets);
std::vector<unsigned int> meshlet_vertices(max_meshlets * max_vertices);
std::vector<unsigned char> meshlet_triangles(max_meshlets * max_triangles * 3);
meshlets.resize(meshopt_buildMeshletsFlex(&meshlets[0], &meshlet_vertices[0], &meshlet_triangles[0], &indices[0], indices.size(), &vertices[0].px, vertices.size(), sizeof(Vertex), max_vertices, min_triangles, max_triangles, 0.f, split_factor));
std::vector<Cluster> clusters(meshlets.size());
for (size_t i = 0; i < meshlets.size(); ++i)
{
const meshopt_Meshlet& meshlet = meshlets[i];
meshopt_optimizeMeshlet(&meshlet_vertices[meshlet.vertex_offset], &meshlet_triangles[meshlet.triangle_offset], meshlet.triangle_count, meshlet.vertex_count);
// note: for now we discard meshlet-local indices; they are valuable for shader code so in the future we should bring them back
clusters[i].indices.resize(meshlet.triangle_count * 3);
for (size_t j = 0; j < meshlet.triangle_count * 3; ++j)
clusters[i].indices[j] = meshlet_vertices[meshlet.vertex_offset + meshlet_triangles[meshlet.triangle_offset + j]];
clusters[i].parent.error = FLT_MAX;
}
return clusters;
}
static std::vector<std::vector<int> > partition(const std::vector<Cluster>& clusters, const std::vector<int>& pending, const std::vector<unsigned int>& remap)
{
if (METIS & 1)
return partitionMetis(clusters, pending, remap);
std::vector<unsigned int> cluster_indices;
std::vector<unsigned int> cluster_counts(pending.size());
size_t total_index_count = 0;
for (size_t i = 0; i < pending.size(); ++i)
total_index_count += clusters[pending[i]].indices.size();
cluster_indices.reserve(total_index_count);
for (size_t i = 0; i < pending.size(); ++i)
{
const Cluster& cluster = clusters[pending[i]];
cluster_counts[i] = unsigned(cluster.indices.size());
for (size_t j = 0; j < cluster.indices.size(); ++j)
cluster_indices.push_back(remap[cluster.indices[j]]);
}
std::vector<unsigned int> cluster_part(pending.size());
size_t partition_count = meshopt_partitionClusters(&cluster_part[0], &cluster_indices[0], cluster_indices.size(), &cluster_counts[0], cluster_counts.size(), remap.size(), kGroupSize);
std::vector<std::vector<int> > partitions(partition_count);
for (size_t i = 0; i < partition_count; ++i)
partitions[i].reserve(kGroupSize + 4);
for (size_t i = 0; i < pending.size(); ++i)
partitions[cluster_part[i]].push_back(pending[i]);
return partitions;
}
static void lockBoundary(std::vector<unsigned char>& locks, const std::vector<std::vector<int> >& groups, const std::vector<Cluster>& clusters, const std::vector<unsigned int>& remap)
{
// for each remapped vertex, keep track of index of the group it's in (or -2 if it's in multiple groups)
std::vector<int> groupmap(locks.size(), -1);
for (size_t i = 0; i < groups.size(); ++i)
for (size_t j = 0; j < groups[i].size(); ++j)
{
const Cluster& cluster = clusters[groups[i][j]];
for (size_t k = 0; k < cluster.indices.size(); ++k)
{
unsigned int v = cluster.indices[k];
unsigned int r = remap[v];
if (groupmap[r] == -1 || groupmap[r] == int(i))
groupmap[r] = int(i);
else
groupmap[r] = -2;
}
}
// note: we need to consistently lock all vertices with the same position to avoid holes
for (size_t i = 0; i < locks.size(); ++i)
{
unsigned int r = remap[i];
locks[i] = (groupmap[r] == -2);
}
}
static std::vector<unsigned int> simplify(const std::vector<Vertex>& vertices, const std::vector<unsigned int>& indices, const std::vector<unsigned char>* locks, size_t target_count, float* error = NULL)
{
if (target_count > indices.size())
return indices;
std::vector<unsigned int> lod(indices.size());
unsigned int options = meshopt_SimplifySparse | meshopt_SimplifyErrorAbsolute;
float normal_weights[3] = {0.5f, 0.5f, 0.5f};
if (kUseNormals)
lod.resize(meshopt_simplifyWithAttributes(&lod[0], &indices[0], indices.size(), &vertices[0].px, vertices.size(), sizeof(Vertex), &vertices[0].nx, sizeof(Vertex), normal_weights, 3, locks ? &(*locks)[0] : NULL, target_count, FLT_MAX, options, error));
else if (locks)
lod.resize(meshopt_simplifyWithAttributes(&lod[0], &indices[0], indices.size(), &vertices[0].px, vertices.size(), sizeof(Vertex), NULL, 0, NULL, 0, &(*locks)[0], target_count, FLT_MAX, options, error));
else
lod.resize(meshopt_simplify(&lod[0], &indices[0], indices.size(), &vertices[0].px, vertices.size(), sizeof(Vertex), target_count, FLT_MAX, options | meshopt_SimplifyLockBorder, error));
return lod;
}
static void dumpMetrics(int level, const std::vector<Cluster>& queue, const std::vector<std::vector<int> >& groups, const std::vector<unsigned int>& remap, const std::vector<unsigned char>& locks, const std::vector<int>& retry);
static bool loadMetis();
void dumpObj(const std::vector<Vertex>& vertices, const std::vector<unsigned int>& indices, bool recomputeNormals = false);
void dumpObj(const char* section, const std::vector<unsigned int>& indices);
void nanite(const std::vector<Vertex>& vertices, const std::vector<unsigned int>& indices)
{
static const char* metis = getenv("METIS");
METIS = metis ? atoi(metis) : 0;
if (METIS)
{
if (loadMetis())
printf("using metis for %s\n", (METIS & 3) == 3 ? "clustering and partition" : ((METIS & 1) ? "partition only" : "clustering only"));
else
printf("metis library is not available\n"), METIS = 0;
}
static const char* dump = getenv("DUMP");
int depth = 0;
std::vector<unsigned char> locks(vertices.size());
// for cluster connectivity, we need a position-only remap that maps vertices with the same position to the same index
// it's more efficient to build it once; unfortunately, meshopt_generateVertexRemap doesn't support stride so we need to use *Multi version
std::vector<unsigned int> remap(vertices.size());
meshopt_Stream position = {&vertices[0].px, sizeof(float) * 3, sizeof(Vertex)};
meshopt_generateVertexRemapMulti(&remap[0], &indices[0], indices.size(), vertices.size(), &position, 1);
// initial clusterization splits the original mesh
std::vector<Cluster> clusters = clusterize(vertices, indices);
for (size_t i = 0; i < clusters.size(); ++i)
clusters[i].self = bounds(vertices, clusters[i].indices, 0.f);
printf("ideal lod chain: %.1f levels\n", log2(double(indices.size() / 3) / double(kClusterSize)));
std::vector<int> pending(clusters.size());
for (size_t i = 0; i < clusters.size(); ++i)
pending[i] = int(i);
// merge and simplify clusters until we can't merge anymore
while (pending.size() > 1)
{
std::vector<std::vector<int> > groups = partition(clusters, pending, remap);
if (kUseLocks)
lockBoundary(locks, groups, clusters, remap);
pending.clear();
std::vector<int> retry;
size_t triangles = 0;
size_t stuck_triangles = 0;
if (dump && depth == atoi(dump))
dumpObj(vertices, std::vector<unsigned int>());
// every group needs to be simplified now
for (size_t i = 0; i < groups.size(); ++i)
{
if (groups[i].empty())
continue; // metis shortcut
std::vector<unsigned int> merged;
for (size_t j = 0; j < groups[i].size(); ++j)
merged.insert(merged.end(), clusters[groups[i][j]].indices.begin(), clusters[groups[i][j]].indices.end());
if (dump && depth == atoi(dump))
{
for (size_t j = 0; j < groups[i].size(); ++j)
dumpObj("cluster", clusters[groups[i][j]].indices);
dumpObj("group", merged);
}
// aim to reduce group size in half
size_t target_size = (merged.size() / 3) / 2 * 3;
float error = 0.f;
std::vector<unsigned int> simplified = simplify(vertices, merged, kUseLocks ? &locks : NULL, target_size, &error);
if (simplified.size() > merged.size() * kSimplifyThreshold)
{
stuck_triangles += merged.size() / 3;
for (size_t j = 0; j < groups[i].size(); ++j)
retry.push_back(groups[i][j]);
continue; // simplification is stuck; abandon the merge
}
// enforce bounds and error monotonicity
// note: it is incorrect to use the precise bounds of the merged or simplified mesh, because this may violate monotonicity
LODBounds groupb = boundsMerge(clusters, groups[i]);
groupb.error += error; // this may overestimate the error, but we are starting from the simplified mesh so this is a little more correct
std::vector<Cluster> split = clusterize(vertices, simplified);
// update parent bounds and error for all clusters in the group
// note that all clusters in the group need to switch simultaneously so they have the same bounds
for (size_t j = 0; j < groups[i].size(); ++j)
{
assert(clusters[groups[i][j]].parent.error == FLT_MAX);
clusters[groups[i][j]].parent = groupb;
}
for (size_t j = 0; j < split.size(); ++j)
{
split[j].self = groupb;
clusters.push_back(split[j]); // std::move
pending.push_back(int(clusters.size()) - 1);
triangles += split[j].indices.size() / 3;
}
}
dumpMetrics(depth, clusters, groups, remap, locks, retry);
depth++;
if (kUseRetry)
{
if (triangles < stuck_triangles / 3)
break;
pending.insert(pending.end(), retry.begin(), retry.end());
}
}
size_t lowest_triangles = 0;
for (size_t i = 0; i < clusters.size(); ++i)
if (clusters[i].parent.error == FLT_MAX)
lowest_triangles += clusters[i].indices.size() / 3;
printf("lowest lod: %d triangles\n", int(lowest_triangles));
// for testing purposes, we can compute a DAG cut from a given viewpoint and dump it as an OBJ
float maxx = 0.f, maxy = 0.f, maxz = 0.f;
for (size_t i = 0; i < vertices.size(); ++i)
{
maxx = std::max(maxx, vertices[i].px * 2);
maxy = std::max(maxy, vertices[i].py * 2);
maxz = std::max(maxz, vertices[i].pz * 2);
}
float threshold = 2e-3f; // 2 pixels at 1080p
float fovy = 60.f;
float znear = 1e-2f;
float proj = 1.f / tanf(fovy * 3.1415926f / 180.f * 0.5f);
std::vector<unsigned int> cut;
for (size_t i = 0; i < clusters.size(); ++i)
if (boundsError(clusters[i].self, maxx, maxy, maxz, proj, znear) <= threshold && boundsError(clusters[i].parent, maxx, maxy, maxz, proj, znear) > threshold)
cut.insert(cut.end(), clusters[i].indices.begin(), clusters[i].indices.end());
printf("cut (%.3f): %d triangles\n", threshold, int(cut.size() / 3));
if (dump && -1 == atoi(dump))
{
dumpObj(vertices, cut);
for (size_t i = 0; i < clusters.size(); ++i)
if (boundsError(clusters[i].self, maxx, maxy, maxz, proj, znear) <= threshold && boundsError(clusters[i].parent, maxx, maxy, maxz, proj, znear) > threshold)
dumpObj("cluster", clusters[i].indices);
}
}
// What follows is code that optionally uses METIS library to perform partitioning and/or clustering.
// The focus of this example is on combining meshopt_ algorithms, but METIS fallbacks are provided for now.
static bool loadMetis()
{
#ifdef _WIN32
return false;
#else
void* handle = dlopen("libmetis.so", RTLD_NOW | RTLD_LOCAL);
if (!handle)
return false;
METIS_SetDefaultOptions = (int (*)(int*))dlsym(handle, "METIS_SetDefaultOptions");
METIS_PartGraphRecursive = (int (*)(int*, int*, int*, int*, int*, int*, int*, int*, float*, float*, int*, int*, int*))dlsym(handle, "METIS_PartGraphRecursive");
return METIS_SetDefaultOptions && METIS_PartGraphRecursive;
#endif
}
static std::vector<Cluster> clusterizeMetis(const std::vector<Vertex>& vertices, const std::vector<unsigned int>& indices)
{
std::vector<unsigned int> shadowib(indices.size());
meshopt_generateShadowIndexBuffer(&shadowib[0], &indices[0], indices.size(), &vertices[0].px, vertices.size(), sizeof(float) * 3, sizeof(Vertex));
std::vector<std::vector<int> > trilist(vertices.size());
for (size_t i = 0; i < indices.size(); ++i)
trilist[shadowib[i]].push_back(int(i / 3));
std::vector<int> xadj(indices.size() / 3 + 1);
std::vector<int> adjncy;
std::vector<int> adjwgt;
std::vector<int> part(indices.size() / 3);
std::vector<int> scratch;
for (size_t i = 0; i < indices.size() / 3; ++i)
{
unsigned int a = shadowib[i * 3 + 0], b = shadowib[i * 3 + 1], c = shadowib[i * 3 + 2];
scratch.clear();
scratch.insert(scratch.end(), trilist[a].begin(), trilist[a].end());
scratch.insert(scratch.end(), trilist[b].begin(), trilist[b].end());
scratch.insert(scratch.end(), trilist[c].begin(), trilist[c].end());
std::sort(scratch.begin(), scratch.end());
for (size_t j = 0; j < scratch.size(); ++j)
{
if (scratch[j] == int(i))
continue;
if (j == 0 || scratch[j] != scratch[j - 1])
{
adjncy.push_back(scratch[j]);
adjwgt.push_back(1);
}
else if (j != 0)
{
assert(scratch[j] == scratch[j - 1]);
adjwgt.back()++;
}
}
xadj[i + 1] = int(adjncy.size());
}
int options[METIS_NOPTIONS];
METIS_SetDefaultOptions(options);
options[METIS_OPTION_SEED] = 42;
options[METIS_OPTION_UFACTOR] = 1; // minimize partition imbalance
// since Metis can't enforce partition sizes, add a little slop to reduce the change we need to split results further
int nvtxs = int(indices.size() / 3);
int ncon = 1;
int nparts = int(indices.size() / 3 + (kClusterSize - kMetisSlop) - 1) / (kClusterSize - kMetisSlop);
int edgecut = 0;
// not sure why this is a special case that we need to handle but okay metis
if (nparts > 1)
{
int r = METIS_PartGraphRecursive(&nvtxs, &ncon, &xadj[0], &adjncy[0], NULL, NULL, &adjwgt[0], &nparts, NULL, NULL, options, &edgecut, &part[0]);
assert(r == METIS_OK);
(void)r;
}
std::vector<Cluster> result(nparts);
for (size_t i = 0; i < indices.size() / 3; ++i)
{
result[part[i]].indices.push_back(indices[i * 3 + 0]);
result[part[i]].indices.push_back(indices[i * 3 + 1]);
result[part[i]].indices.push_back(indices[i * 3 + 2]);
}
for (int i = 0; i < nparts; ++i)
{
result[i].parent.error = FLT_MAX;
// need to split the cluster further...
// this could use meshopt but we're trying to get a complete baseline from metis
if (result[i].indices.size() > kClusterSize * 3)
{
std::vector<Cluster> splits = clusterizeMetis(vertices, result[i].indices);
assert(splits.size() > 1);
result[i] = splits[0];
for (size_t j = 1; j < splits.size(); ++j)
result.push_back(splits[j]);
}
}
return result;
}
static std::vector<std::vector<int> > partitionMetis(const std::vector<Cluster>& clusters, const std::vector<int>& pending, const std::vector<unsigned int>& remap)
{
std::vector<std::vector<int> > result;
std::vector<std::vector<int> > vertices(remap.size());
for (size_t i = 0; i < pending.size(); ++i)
{
const Cluster& cluster = clusters[pending[i]];
for (size_t j = 0; j < cluster.indices.size(); ++j)
{
int v = remap[cluster.indices[j]];
std::vector<int>& list = vertices[v];
if (list.empty() || list.back() != int(i))
list.push_back(int(i));
}
}
std::map<std::pair<int, int>, int> adjacency;
for (size_t v = 0; v < vertices.size(); ++v)
{
const std::vector<int>& list = vertices[v];
for (size_t i = 0; i < list.size(); ++i)
for (size_t j = i + 1; j < list.size(); ++j)
adjacency[std::make_pair(std::min(list[i], list[j]), std::max(list[i], list[j]))]++;
}
std::vector<std::vector<std::pair<int, int> > > neighbors(pending.size());
for (std::map<std::pair<int, int>, int>::iterator it = adjacency.begin(); it != adjacency.end(); ++it)
{
neighbors[it->first.first].push_back(std::make_pair(it->first.second, it->second));
neighbors[it->first.second].push_back(std::make_pair(it->first.first, it->second));
}
std::vector<int> xadj(pending.size() + 1);
std::vector<int> adjncy;
std::vector<int> adjwgt;
std::vector<int> part(pending.size());
for (size_t i = 0; i < pending.size(); ++i)
{
for (size_t j = 0; j < neighbors[i].size(); ++j)
{
adjncy.push_back(neighbors[i][j].first);
adjwgt.push_back(neighbors[i][j].second);
}
xadj[i + 1] = int(adjncy.size());
}
int options[METIS_NOPTIONS];
METIS_SetDefaultOptions(options);
options[METIS_OPTION_SEED] = 42;
options[METIS_OPTION_UFACTOR] = 100;
int nvtxs = int(pending.size());
int ncon = 1;
int nparts = int(pending.size() + kGroupSize - 1) / kGroupSize;
int edgecut = 0;
// not sure why this is a special case that we need to handle but okay metis
if (nparts > 1)
{
int r = METIS_PartGraphRecursive(&nvtxs, &ncon, &xadj[0], &adjncy[0], NULL, NULL, &adjwgt[0], &nparts, NULL, NULL, options, &edgecut, &part[0]);
assert(r == METIS_OK);
(void)r;
}
result.resize(nparts);
for (size_t i = 0; i < part.size(); ++i)
result[part[i]].push_back(pending[i]);
return result;
}
// What follows is code that is helpful for collecting metrics, visualizing cuts, etc.
// This code is not used in the actual clustering implementation and can be ignored.
static int follow(std::vector<int>& parents, int index)
{
while (index != parents[index])
{
int parent = parents[index];
parents[index] = parents[parent];
index = parent;
}
return index;
}
static int measureComponents(std::vector<int>& parents, const std::vector<unsigned int>& indices, const std::vector<unsigned int>& remap)
{
assert(parents.size() == remap.size());
for (size_t i = 0; i < indices.size(); ++i)
{
unsigned int v = remap[indices[i]];
parents[v] = v;
}
for (size_t i = 0; i < indices.size(); ++i)
{
int v0 = remap[indices[i]];
int v1 = remap[indices[i + (i % 3 == 2 ? -2 : 1)]];
v0 = follow(parents, v0);
v1 = follow(parents, v1);
parents[v0] = v1;
}
for (size_t i = 0; i < indices.size(); ++i)
{
unsigned int v = remap[indices[i]];
parents[v] = follow(parents, v);
}
int roots = 0;
for (size_t i = 0; i < indices.size(); ++i)
{
unsigned int v = remap[indices[i]];
roots += parents[v] == int(v);
parents[v] = -1; // make sure we only count each root once
}
return roots;
}
static int measureUnique(std::vector<int>& used, const std::vector<unsigned int>& indices, const std::vector<unsigned char>* locks = NULL)
{
for (size_t i = 0; i < indices.size(); ++i)
{
unsigned int v = indices[i];
used[v] = 1;
}
size_t vertices = 0;
for (size_t i = 0; i < indices.size(); ++i)
{
unsigned int v = indices[i];
vertices += used[v] && (!locks || (*locks)[v]);
used[v] = 0;
}
return int(vertices);
}
static void dumpMetrics(int level, const std::vector<Cluster>& queue, const std::vector<std::vector<int> >& groups, const std::vector<unsigned int>& remap, const std::vector<unsigned char>& locks, const std::vector<int>& retry)
{
std::vector<int> parents(remap.size());
int clusters = 0;
int triangles = 0;
int full_clusters = 0;
int components = 0;
int xformed = 0;
int boundary = 0;
for (size_t i = 0; i < groups.size(); ++i)
{
for (size_t j = 0; j < groups[i].size(); ++j)
{
const Cluster& cluster = queue[groups[i][j]];
clusters++;
triangles += int(cluster.indices.size() / 3);
full_clusters += cluster.indices.size() == kClusterSize * 3;
components += measureComponents(parents, cluster.indices, remap);
xformed += measureUnique(parents, cluster.indices);
boundary += kUseLocks ? measureUnique(parents, cluster.indices, &locks) : 0;
}
}
int stuck_clusters = 0;
int stuck_triangles = 0;
for (size_t i = 0; i < retry.size(); ++i)
{
const Cluster& cluster = queue[retry[i]];
stuck_clusters++;
stuck_triangles += int(cluster.indices.size() / 3);
}
double avg_group = double(clusters) / double(groups.size());
double inv_clusters = 1.0 / double(clusters);
printf("lod %d: %d clusters (%.1f%% full, %.1f tri/cl, %.1f vtx/cl, %.2f connected, %.1f boundary, %.1f partition), %d triangles",
level, clusters,
double(full_clusters) * inv_clusters * 100, double(triangles) * inv_clusters, double(xformed) * inv_clusters, double(components) * inv_clusters, double(boundary) * inv_clusters, avg_group,
int(triangles));
if (stuck_clusters)
printf("; stuck %d clusters (%d triangles)", stuck_clusters, stuck_triangles);
printf("\n");
}