// Copyright 2011 Google Inc. All Rights Reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); you // may not use this file except in compliance with the License. You // may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or // implied. See the License for the specific language governing // permissions and limitations under the License. #ifndef WEBGL_LOADER_OPTIMIZE_H_ #define WEBGL_LOADER_OPTIMIZE_H_ #include #include #include #include "base.h" // TODO: since most vertices are part of 6 faces, you can optimize // this by using a small inline buffer. typedef std::vector FaceList; // Linear-Speed Vertex Cache Optimisation, via: // http://home.comcast.net/~tom_forsyth/papers/fast_vert_cache_opt.html class VertexOptimizer { public: struct TriangleData { bool active; // true iff triangle has not been optimized and emitted. // TODO: eliminate some wasted computation by using this cache. // float score; }; VertexOptimizer(const QuantizedAttribList& attribs) : attribs_(attribs), per_vertex_(attribs_.size() / 8), next_unused_index_(0) { // The cache has an extra slot allocated to simplify the logic in // InsertIndexToCache. for (unsigned int i = 0; i < kCacheSize + 1; ++i) { cache_[i] = kUnknownIndex; } // Initialize per-vertex state. for (size_t i = 0; i < per_vertex_.size(); ++i) { VertexData& vertex_data = per_vertex_[i]; vertex_data.cache_tag = kCacheSize; vertex_data.output_index = kMaxOutputIndex; } } void AddTriangles(const int* indices, size_t length, WebGLMeshList* meshes) { std::vector per_tri(length / 3); // Loop through the triangles, updating vertex->face lists. for (size_t i = 0; i < per_tri.size(); ++i) { per_tri[i].active = true; per_vertex_[indices[3*i + 0]].faces.push_back(i); per_vertex_[indices[3*i + 1]].faces.push_back(i); per_vertex_[indices[3*i + 2]].faces.push_back(i); } // TODO: with index bounds, no need to recompute everything. // Compute initial vertex scores. for (size_t i = 0; i < per_vertex_.size(); ++i) { VertexData& vertex_data = per_vertex_[i]; vertex_data.cache_tag = kCacheSize; vertex_data.output_index = kMaxOutputIndex; vertex_data.UpdateScore(); } // Prepare output. if (meshes->empty()) { meshes->push_back(WebGLMesh()); } WebGLMesh* mesh = &meshes->back(); // Consume indices, one triangle at a time. for (size_t c = 0; c < per_tri.size(); ++c) { const int best_triangle = FindBestTriangle(indices, per_tri); per_tri[best_triangle].active = false; // Iterate through triangle indices. for (size_t i = 0; i < 3; ++i) { const int index = indices[3*best_triangle + i]; VertexData& vertex_data = per_vertex_[index]; vertex_data.RemoveFace(best_triangle); InsertIndexToCache(index); const int cached_output_index = per_vertex_[index].output_index; // Have we seen this index before? if (cached_output_index != kMaxOutputIndex) { mesh->indices.push_back(cached_output_index); continue; } // The first time we see an index, not only do we increment // next_unused_index_ counter, but we must also copy the // corresponding attributes. TODO: do quantization here? per_vertex_[index].output_index = next_unused_index_; for (size_t j = 0; j < 8; ++j) { mesh->attribs.push_back(attribs_[8*index + j]); } mesh->indices.push_back(next_unused_index_++); } // Check if there is room for another triangle. if (next_unused_index_ > kMaxOutputIndex - 3) { // Is it worth figuring out which other triangles can be added // given the verties already added? Then, perhaps // re-optimizing? next_unused_index_ = 0; meshes->push_back(WebGLMesh()); mesh = &meshes->back(); for (size_t i = 0; i <= kCacheSize; ++i) { cache_[i] = kUnknownIndex; } for (size_t i = 0; i < per_vertex_.size(); ++i) { per_vertex_[i].output_index = kMaxOutputIndex; } } } } private: static const int kUnknownIndex = -1; static const uint16 kMaxOutputIndex = 0xD800; static const size_t kCacheSize = 32; // Does larger improve compression? struct VertexData { // Should this also update scores for incident triangles? void UpdateScore() { const size_t active_tris = faces.size(); if (active_tris <= 0) { score = -1.f; return; } // TODO: build initial score table. if (cache_tag < 3) { // The most recent triangle should has a fixed score to // discourage generating nothing but really long strips. If we // want strips, we should use a different optimizer. const float kLastTriScore = 0.75f; score = kLastTriScore; } else if (cache_tag < kCacheSize) { // Points for being recently used. const float kScale = 1.f / (kCacheSize - 3); const float kCacheDecayPower = 1.5f; score = powf(1.f - kScale * (cache_tag - 3), kCacheDecayPower); } else { // Not in cache. score = 0.f; } // Bonus points for having a low number of tris still to use the // vert, so we get rid of lone verts quickly. const float kValenceBoostScale = 2.0f; const float kValenceBoostPower = 0.5f; // rsqrt? const float valence_boost = powf(active_tris, -kValenceBoostPower); score += valence_boost * kValenceBoostScale; } // TODO: this assumes that "tri" is in the list! void RemoveFace(int tri) { FaceList::iterator face = faces.begin(); while (*face != tri) ++face; *face = faces.back(); faces.pop_back(); } FaceList faces; unsigned int cache_tag; // kCacheSize means not in cache. float score; uint16 output_index; }; int FindBestTriangle(const int* indices, const std::vector& per_tri) { float best_score = -HUGE_VALF; int best_triangle = -1; // The trick to making this algorithm run in linear time (with // respect to the vertices) is to only scan the triangles incident // on the simulated cache for the next triangle. It is an // approximation, but the score is heuristic. Anyway, most of the // time the best triangle will be found this way. for (size_t i = 0; i < kCacheSize; ++i) { if (cache_[i] == kUnknownIndex) { break; } const VertexData& vertex_data = per_vertex_[cache_[i]]; for (size_t j = 0; j < vertex_data.faces.size(); ++j) { const int tri_index = vertex_data.faces[j]; if (per_tri[tri_index].active) { const float score = per_vertex_[indices[3*tri_index + 0]].score + per_vertex_[indices[3*tri_index + 1]].score + per_vertex_[indices[3*tri_index + 2]].score; if (score > best_score) { best_score = score; best_triangle = tri_index; } } } } // TODO: keep a range of active triangles to make the slow scan a // little faster. Does this ever happen? if (best_triangle == -1) { // If no triangles can be found through the cache (e.g. for the // first triangle) go through all the active triangles and find // the best one. for (size_t i = 0; i < per_tri.size(); ++i) { if (per_tri[i].active) { const float score = per_vertex_[indices[3*i + 0]].score + per_vertex_[indices[3*i + 1]].score + per_vertex_[indices[3*i + 2]].score; if (score > best_score) { best_score = score; best_triangle = i; } } } CHECK(-1 != best_triangle); } return best_triangle; } // TODO: faster to update an entire triangle. // This also updates the vertex scores! void InsertIndexToCache(int index) { // Find how recently the vertex was used. const unsigned int cache_tag = per_vertex_[index].cache_tag; // Don't do anything if the vertex is already at the head of the // LRU list. if (cache_tag == 0) return; // Loop through the cache, inserting the index at the front, and // bubbling down to where the index was originally found. If the // index was not originally in the cache, then it claims to be at // the (kCacheSize + 1)th entry, and we use an extra slot to make // that case simpler. int to_insert = index; for (unsigned int i = 0; i <= cache_tag; ++i) { const int current_index = cache_[i]; // Update cross references between the entry of the cache and // the per-vertex data. cache_[i] = to_insert; per_vertex_[to_insert].cache_tag = i; per_vertex_[to_insert].UpdateScore(); // No need to continue if we find an empty entry. if (current_index == kUnknownIndex) { break; } to_insert = current_index; } } const QuantizedAttribList& attribs_; std::vector per_vertex_; int cache_[kCacheSize + 1]; uint16 next_unused_index_; }; #endif // WEBGL_LOADER_OPTIMIZE_H_