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