334 lines
11 KiB
C++
334 lines
11 KiB
C++
// This file is part of meshoptimizer library; see meshoptimizer.h for version/license details
|
|
#include "meshoptimizer.h"
|
|
|
|
#include <assert.h>
|
|
#include <math.h>
|
|
#include <string.h>
|
|
|
|
// This work is based on:
|
|
// Pedro Sander, Diego Nehab and Joshua Barczak. Fast Triangle Reordering for Vertex Locality and Reduced Overdraw. 2007
|
|
namespace meshopt
|
|
{
|
|
|
|
static void calculateSortData(float* sort_data, const unsigned int* indices, size_t index_count, const float* vertex_positions, size_t vertex_positions_stride, const unsigned int* clusters, size_t cluster_count)
|
|
{
|
|
size_t vertex_stride_float = vertex_positions_stride / sizeof(float);
|
|
|
|
float mesh_centroid[3] = {};
|
|
|
|
for (size_t i = 0; i < index_count; ++i)
|
|
{
|
|
const float* p = vertex_positions + vertex_stride_float * indices[i];
|
|
|
|
mesh_centroid[0] += p[0];
|
|
mesh_centroid[1] += p[1];
|
|
mesh_centroid[2] += p[2];
|
|
}
|
|
|
|
mesh_centroid[0] /= index_count;
|
|
mesh_centroid[1] /= index_count;
|
|
mesh_centroid[2] /= index_count;
|
|
|
|
for (size_t cluster = 0; cluster < cluster_count; ++cluster)
|
|
{
|
|
size_t cluster_begin = clusters[cluster] * 3;
|
|
size_t cluster_end = (cluster + 1 < cluster_count) ? clusters[cluster + 1] * 3 : index_count;
|
|
assert(cluster_begin < cluster_end);
|
|
|
|
float cluster_area = 0;
|
|
float cluster_centroid[3] = {};
|
|
float cluster_normal[3] = {};
|
|
|
|
for (size_t i = cluster_begin; i < cluster_end; i += 3)
|
|
{
|
|
const float* p0 = vertex_positions + vertex_stride_float * indices[i + 0];
|
|
const float* p1 = vertex_positions + vertex_stride_float * indices[i + 1];
|
|
const float* p2 = vertex_positions + vertex_stride_float * indices[i + 2];
|
|
|
|
float p10[3] = {p1[0] - p0[0], p1[1] - p0[1], p1[2] - p0[2]};
|
|
float p20[3] = {p2[0] - p0[0], p2[1] - p0[1], p2[2] - p0[2]};
|
|
|
|
float normalx = p10[1] * p20[2] - p10[2] * p20[1];
|
|
float normaly = p10[2] * p20[0] - p10[0] * p20[2];
|
|
float normalz = p10[0] * p20[1] - p10[1] * p20[0];
|
|
|
|
float area = sqrtf(normalx * normalx + normaly * normaly + normalz * normalz);
|
|
|
|
cluster_centroid[0] += (p0[0] + p1[0] + p2[0]) * (area / 3);
|
|
cluster_centroid[1] += (p0[1] + p1[1] + p2[1]) * (area / 3);
|
|
cluster_centroid[2] += (p0[2] + p1[2] + p2[2]) * (area / 3);
|
|
cluster_normal[0] += normalx;
|
|
cluster_normal[1] += normaly;
|
|
cluster_normal[2] += normalz;
|
|
cluster_area += area;
|
|
}
|
|
|
|
float inv_cluster_area = cluster_area == 0 ? 0 : 1 / cluster_area;
|
|
|
|
cluster_centroid[0] *= inv_cluster_area;
|
|
cluster_centroid[1] *= inv_cluster_area;
|
|
cluster_centroid[2] *= inv_cluster_area;
|
|
|
|
float cluster_normal_length = sqrtf(cluster_normal[0] * cluster_normal[0] + cluster_normal[1] * cluster_normal[1] + cluster_normal[2] * cluster_normal[2]);
|
|
float inv_cluster_normal_length = cluster_normal_length == 0 ? 0 : 1 / cluster_normal_length;
|
|
|
|
cluster_normal[0] *= inv_cluster_normal_length;
|
|
cluster_normal[1] *= inv_cluster_normal_length;
|
|
cluster_normal[2] *= inv_cluster_normal_length;
|
|
|
|
float centroid_vector[3] = {cluster_centroid[0] - mesh_centroid[0], cluster_centroid[1] - mesh_centroid[1], cluster_centroid[2] - mesh_centroid[2]};
|
|
|
|
sort_data[cluster] = centroid_vector[0] * cluster_normal[0] + centroid_vector[1] * cluster_normal[1] + centroid_vector[2] * cluster_normal[2];
|
|
}
|
|
}
|
|
|
|
static void calculateSortOrderRadix(unsigned int* sort_order, const float* sort_data, unsigned short* sort_keys, size_t cluster_count)
|
|
{
|
|
// compute sort data bounds and renormalize, using fixed point snorm
|
|
float sort_data_max = 1e-3f;
|
|
|
|
for (size_t i = 0; i < cluster_count; ++i)
|
|
{
|
|
float dpa = fabsf(sort_data[i]);
|
|
|
|
sort_data_max = (sort_data_max < dpa) ? dpa : sort_data_max;
|
|
}
|
|
|
|
const int sort_bits = 11;
|
|
|
|
for (size_t i = 0; i < cluster_count; ++i)
|
|
{
|
|
// note that we flip distribution since high dot product should come first
|
|
float sort_key = 0.5f - 0.5f * (sort_data[i] / sort_data_max);
|
|
|
|
sort_keys[i] = meshopt_quantizeUnorm(sort_key, sort_bits) & ((1 << sort_bits) - 1);
|
|
}
|
|
|
|
// fill histogram for counting sort
|
|
unsigned int histogram[1 << sort_bits];
|
|
memset(histogram, 0, sizeof(histogram));
|
|
|
|
for (size_t i = 0; i < cluster_count; ++i)
|
|
{
|
|
histogram[sort_keys[i]]++;
|
|
}
|
|
|
|
// compute offsets based on histogram data
|
|
size_t histogram_sum = 0;
|
|
|
|
for (size_t i = 0; i < 1 << sort_bits; ++i)
|
|
{
|
|
size_t count = histogram[i];
|
|
histogram[i] = unsigned(histogram_sum);
|
|
histogram_sum += count;
|
|
}
|
|
|
|
assert(histogram_sum == cluster_count);
|
|
|
|
// compute sort order based on offsets
|
|
for (size_t i = 0; i < cluster_count; ++i)
|
|
{
|
|
sort_order[histogram[sort_keys[i]]++] = unsigned(i);
|
|
}
|
|
}
|
|
|
|
static unsigned int updateCache(unsigned int a, unsigned int b, unsigned int c, unsigned int cache_size, unsigned int* cache_timestamps, unsigned int& timestamp)
|
|
{
|
|
unsigned int cache_misses = 0;
|
|
|
|
// if vertex is not in cache, put it in cache
|
|
if (timestamp - cache_timestamps[a] > cache_size)
|
|
{
|
|
cache_timestamps[a] = timestamp++;
|
|
cache_misses++;
|
|
}
|
|
|
|
if (timestamp - cache_timestamps[b] > cache_size)
|
|
{
|
|
cache_timestamps[b] = timestamp++;
|
|
cache_misses++;
|
|
}
|
|
|
|
if (timestamp - cache_timestamps[c] > cache_size)
|
|
{
|
|
cache_timestamps[c] = timestamp++;
|
|
cache_misses++;
|
|
}
|
|
|
|
return cache_misses;
|
|
}
|
|
|
|
static size_t generateHardBoundaries(unsigned int* destination, const unsigned int* indices, size_t index_count, size_t vertex_count, unsigned int cache_size, unsigned int* cache_timestamps)
|
|
{
|
|
memset(cache_timestamps, 0, vertex_count * sizeof(unsigned int));
|
|
|
|
unsigned int timestamp = cache_size + 1;
|
|
|
|
size_t face_count = index_count / 3;
|
|
|
|
size_t result = 0;
|
|
|
|
for (size_t i = 0; i < face_count; ++i)
|
|
{
|
|
unsigned int m = updateCache(indices[i * 3 + 0], indices[i * 3 + 1], indices[i * 3 + 2], cache_size, &cache_timestamps[0], timestamp);
|
|
|
|
// when all three vertices are not in the cache it's usually relatively safe to assume that this is a new patch in the mesh
|
|
// that is disjoint from previous vertices; sometimes it might come back to reference existing vertices but that frequently
|
|
// suggests an inefficiency in the vertex cache optimization algorithm
|
|
// usually the first triangle has 3 misses unless it's degenerate - thus we make sure the first cluster always starts with 0
|
|
if (i == 0 || m == 3)
|
|
{
|
|
destination[result++] = unsigned(i);
|
|
}
|
|
}
|
|
|
|
assert(result <= index_count / 3);
|
|
|
|
return result;
|
|
}
|
|
|
|
static size_t generateSoftBoundaries(unsigned int* destination, const unsigned int* indices, size_t index_count, size_t vertex_count, const unsigned int* clusters, size_t cluster_count, unsigned int cache_size, float threshold, unsigned int* cache_timestamps)
|
|
{
|
|
memset(cache_timestamps, 0, vertex_count * sizeof(unsigned int));
|
|
|
|
unsigned int timestamp = 0;
|
|
|
|
size_t result = 0;
|
|
|
|
for (size_t it = 0; it < cluster_count; ++it)
|
|
{
|
|
size_t start = clusters[it];
|
|
size_t end = (it + 1 < cluster_count) ? clusters[it + 1] : index_count / 3;
|
|
assert(start < end);
|
|
|
|
// reset cache
|
|
timestamp += cache_size + 1;
|
|
|
|
// measure cluster ACMR
|
|
unsigned int cluster_misses = 0;
|
|
|
|
for (size_t i = start; i < end; ++i)
|
|
{
|
|
unsigned int m = updateCache(indices[i * 3 + 0], indices[i * 3 + 1], indices[i * 3 + 2], cache_size, &cache_timestamps[0], timestamp);
|
|
|
|
cluster_misses += m;
|
|
}
|
|
|
|
float cluster_threshold = threshold * (float(cluster_misses) / float(end - start));
|
|
|
|
// first cluster always starts from the hard cluster boundary
|
|
destination[result++] = unsigned(start);
|
|
|
|
// reset cache
|
|
timestamp += cache_size + 1;
|
|
|
|
unsigned int running_misses = 0;
|
|
unsigned int running_faces = 0;
|
|
|
|
for (size_t i = start; i < end; ++i)
|
|
{
|
|
unsigned int m = updateCache(indices[i * 3 + 0], indices[i * 3 + 1], indices[i * 3 + 2], cache_size, &cache_timestamps[0], timestamp);
|
|
|
|
running_misses += m;
|
|
running_faces += 1;
|
|
|
|
if (float(running_misses) / float(running_faces) <= cluster_threshold)
|
|
{
|
|
// we have reached the target ACMR with the current triangle so we need to start a new cluster on the next one
|
|
// note that this may mean that we add 'end` to destination for the last triangle, which will imply that the last
|
|
// cluster is empty; however, the 'pop_back' after the loop will clean it up
|
|
destination[result++] = unsigned(i + 1);
|
|
|
|
// reset cache
|
|
timestamp += cache_size + 1;
|
|
|
|
running_misses = 0;
|
|
running_faces = 0;
|
|
}
|
|
}
|
|
|
|
// each time we reach the target ACMR we flush the cluster
|
|
// this means that the last cluster is by definition not very good - there are frequent cases where we are left with a few triangles
|
|
// in the last cluster, producing a very bad ACMR and significantly penalizing the overall results
|
|
// thus we remove the last cluster boundary, merging the last complete cluster with the last incomplete one
|
|
// there are sometimes cases when the last cluster is actually good enough - in which case the code above would have added 'end'
|
|
// to the cluster boundary array which we need to remove anyway - this code will do that automatically
|
|
if (destination[result - 1] != start)
|
|
{
|
|
result--;
|
|
}
|
|
}
|
|
|
|
assert(result >= cluster_count);
|
|
assert(result <= index_count / 3);
|
|
|
|
return result;
|
|
}
|
|
|
|
} // namespace meshopt
|
|
|
|
void meshopt_optimizeOverdraw(unsigned int* destination, const unsigned int* indices, size_t index_count, const float* vertex_positions, size_t vertex_count, size_t vertex_positions_stride, float threshold)
|
|
{
|
|
using namespace meshopt;
|
|
|
|
assert(index_count % 3 == 0);
|
|
assert(vertex_positions_stride >= 12 && vertex_positions_stride <= 256);
|
|
assert(vertex_positions_stride % sizeof(float) == 0);
|
|
|
|
meshopt_Allocator allocator;
|
|
|
|
// guard for empty meshes
|
|
if (index_count == 0 || vertex_count == 0)
|
|
return;
|
|
|
|
// support in-place optimization
|
|
if (destination == indices)
|
|
{
|
|
unsigned int* indices_copy = allocator.allocate<unsigned int>(index_count);
|
|
memcpy(indices_copy, indices, index_count * sizeof(unsigned int));
|
|
indices = indices_copy;
|
|
}
|
|
|
|
unsigned int cache_size = 16;
|
|
|
|
unsigned int* cache_timestamps = allocator.allocate<unsigned int>(vertex_count);
|
|
|
|
// generate hard boundaries from full-triangle cache misses
|
|
unsigned int* hard_clusters = allocator.allocate<unsigned int>(index_count / 3);
|
|
size_t hard_cluster_count = generateHardBoundaries(hard_clusters, indices, index_count, vertex_count, cache_size, cache_timestamps);
|
|
|
|
// generate soft boundaries
|
|
unsigned int* soft_clusters = allocator.allocate<unsigned int>(index_count / 3 + 1);
|
|
size_t soft_cluster_count = generateSoftBoundaries(soft_clusters, indices, index_count, vertex_count, hard_clusters, hard_cluster_count, cache_size, threshold, cache_timestamps);
|
|
|
|
const unsigned int* clusters = soft_clusters;
|
|
size_t cluster_count = soft_cluster_count;
|
|
|
|
// fill sort data
|
|
float* sort_data = allocator.allocate<float>(cluster_count);
|
|
calculateSortData(sort_data, indices, index_count, vertex_positions, vertex_positions_stride, clusters, cluster_count);
|
|
|
|
// sort clusters using sort data
|
|
unsigned short* sort_keys = allocator.allocate<unsigned short>(cluster_count);
|
|
unsigned int* sort_order = allocator.allocate<unsigned int>(cluster_count);
|
|
calculateSortOrderRadix(sort_order, sort_data, sort_keys, cluster_count);
|
|
|
|
// fill output buffer
|
|
size_t offset = 0;
|
|
|
|
for (size_t it = 0; it < cluster_count; ++it)
|
|
{
|
|
unsigned int cluster = sort_order[it];
|
|
assert(cluster < cluster_count);
|
|
|
|
size_t cluster_begin = clusters[cluster] * 3;
|
|
size_t cluster_end = (cluster + 1 < cluster_count) ? clusters[cluster + 1] * 3 : index_count;
|
|
assert(cluster_begin < cluster_end);
|
|
|
|
memcpy(destination + offset, indices + cluster_begin, (cluster_end - cluster_begin) * sizeof(unsigned int));
|
|
offset += cluster_end - cluster_begin;
|
|
}
|
|
|
|
assert(offset == index_count);
|
|
}
|