| // Copyright (c) 2022, ETH Zurich and UNC Chapel Hill. | |
| // All rights reserved. | |
| // | |
| // Redistribution and use in source and binary forms, with or without | |
| // modification, are permitted provided that the following conditions are met: | |
| // | |
| // * Redistributions of source code must retain the above copyright | |
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| // | |
| // * Redistributions in binary form must reproduce the above copyright | |
| // notice, this list of conditions and the following disclaimer in the | |
| // documentation and/or other materials provided with the distribution. | |
| // | |
| // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of | |
| // its contributors may be used to endorse or promote products derived | |
| // from this software without specific prior written permission. | |
| // | |
| // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" | |
| // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE | |
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| // | |
| // Author: Johannes L. Schoenberger (jsch-at-demuc-dot-de) | |
| namespace colmap { | |
| namespace retrieval { | |
| struct ImageScore { | |
| int image_id = -1; | |
| float score = 0.0f; | |
| }; | |
| // Implements the weighting function used to derive a voting weight from the | |
| // Hamming distance of two binary signatures. See Eqn. 4 in | |
| // Arandjelovic, Zisserman. DisLocation: Scalable descriptor distinctiveness for | |
| // location recognition. ACCV 2014. | |
| // The template is the length of the Hamming embedding vectors. | |
| // This class is based on an original implementation by Torsten Sattler. | |
| template <int N, int kSigma = 16> | |
| class HammingDistWeightFunctor { | |
| public: | |
| static const size_t kMaxHammingDistance = static_cast<size_t>(1.5f * kSigma); | |
| HammingDistWeightFunctor() { | |
| // Fills the look-up table. | |
| const float sigma_squared = kSigma * kSigma; | |
| for (int n = 0; n <= N; ++n) { | |
| const float hamming_dist = static_cast<float>(n); | |
| if (hamming_dist <= kMaxHammingDistance) { | |
| look_up_table_.at(n) = | |
| std::exp(-hamming_dist * hamming_dist / sigma_squared); | |
| } else { | |
| look_up_table_.at(n) = 0.0f; | |
| } | |
| } | |
| } | |
| // Returns the weight for Hamming distance h and standard deviation sigma. | |
| // Does not perform a range check when performing the look-up. | |
| inline float operator()(const size_t hamming_dist) const { | |
| return look_up_table_.at(hamming_dist); | |
| } | |
| private: | |
| // In order to avoid wasting computations, we once compute a look-up table | |
| // storing all function values for all possible values of the standard | |
| // deviation \sigma. This is implemented as a (N + 1) vector. | |
| std::array<float, N + 1> look_up_table_; | |
| }; | |
| } // namespace retrieval | |
| } // namespace colmap | |