godot/thirdparty/meshoptimizer/overdrawoptimizer.cpp

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// 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 > 0 && 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);
}