| // 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 | |
| // notice, this list of conditions and the following disclaimer. | |
| // | |
| // * 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 | |
| // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE | |
| // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE | |
| // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR | |
| // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF | |
| // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS | |
| // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN | |
| // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) | |
| // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE | |
| // POSSIBILITY OF SUCH DAMAGE. | |
| // | |
| // Author: Johannes L. Schoenberger (jsch-at-demuc-dot-de) | |
| namespace colmap { | |
| namespace mvs { | |
| class GpuMatRefImage { | |
| public: | |
| GpuMatRefImage(const size_t width, const size_t height); | |
| // Filter image using sum convolution kernel to compute local sum of | |
| // intensities. The filtered images can then be used for repeated, efficient | |
| // NCC computation. | |
| void Filter(const uint8_t* image_data, const size_t window_radius, | |
| const size_t window_step, const float sigma_spatial, | |
| const float sigma_color); | |
| // Image intensities. | |
| std::unique_ptr<GpuMat<uint8_t>> image; | |
| // Local sum of image intensities. | |
| std::unique_ptr<GpuMat<float>> sum_image; | |
| // Local sum of squared image intensities. | |
| std::unique_ptr<GpuMat<float>> squared_sum_image; | |
| private: | |
| const static size_t kBlockDimX = 16; | |
| const static size_t kBlockDimY = 12; | |
| size_t width_; | |
| size_t height_; | |
| }; | |
| struct BilateralWeightComputer { | |
| __device__ BilateralWeightComputer(const float sigma_spatial, | |
| const float sigma_color) | |
| : spatial_normalization_(1.0f / (2.0f * sigma_spatial * sigma_spatial)), | |
| color_normalization_(1.0f / (2.0f * sigma_color * sigma_color)) {} | |
| __device__ inline float Compute(const float row_diff, const float col_diff, | |
| const float color1, | |
| const float color2) const { | |
| const float spatial_dist_squared = | |
| row_diff * row_diff + col_diff * col_diff; | |
| const float color_dist = color1 - color2; | |
| return exp(-spatial_dist_squared * spatial_normalization_ - | |
| color_dist * color_dist * color_normalization_); | |
| } | |
| private: | |
| const float spatial_normalization_; | |
| const float color_normalization_; | |
| }; | |
| } // namespace mvs | |
| } // namespace colmap | |