1 // Copyright (c) 2018, ETH Zurich and UNC Chapel Hill. 2 // All rights reserved. 3 // 4 // Redistribution and use in source and binary forms, with or without 5 // modification, are permitted provided that the following conditions are met: 6 // 7 // * Redistributions of source code must retain the above copyright 8 // notice, this list of conditions and the following disclaimer. 9 // 10 // * Redistributions in binary form must reproduce the above copyright 11 // notice, this list of conditions and the following disclaimer in the 12 // documentation and/or other materials provided with the distribution. 13 // 14 // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of 15 // its contributors may be used to endorse or promote products derived 16 // from this software without specific prior written permission. 17 // 18 // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 19 // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 20 // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE 21 // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE 22 // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR 23 // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF 24 // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS 25 // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN 26 // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) 27 // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 28 // POSSIBILITY OF SUCH DAMAGE. 29 // 30 // Author: Johannes L. Schoenberger (jsch-at-demuc-dot-de) 31 32 #ifndef COLMAP_SRC_MVS_GPU_MAT_REF_IMAGE_H_ 33 #define COLMAP_SRC_MVS_GPU_MAT_REF_IMAGE_H_ 34 35 #include <memory> 36 37 #include "mvs/cuda_array_wrapper.h" 38 #include "mvs/gpu_mat.h" 39 40 namespace colmap { 41 namespace mvs { 42 43 class GpuMatRefImage { 44 public: 45 GpuMatRefImage(const size_t width, const size_t height); 46 47 // Filter image using sum convolution kernel to compute local sum of 48 // intensities. The filtered images can then be used for repeated, efficient 49 // NCC computation. 50 void Filter(const uint8_t* image_data, const size_t window_radius, 51 const size_t window_step, const float sigma_spatial, 52 const float sigma_color); 53 54 // Image intensities. 55 std::unique_ptr<GpuMat<uint8_t>> image; 56 57 // Local sum of image intensities. 58 std::unique_ptr<GpuMat<float>> sum_image; 59 60 // Local sum of squared image intensities. 61 std::unique_ptr<GpuMat<float>> squared_sum_image; 62 63 private: 64 const static size_t kBlockDimX = 16; 65 const static size_t kBlockDimY = 12; 66 67 size_t width_; 68 size_t height_; 69 }; 70 71 struct BilateralWeightComputer { BilateralWeightComputerBilateralWeightComputer72 __device__ BilateralWeightComputer(const float sigma_spatial, 73 const float sigma_color) 74 : spatial_normalization_(1.0f / (2.0f * sigma_spatial * sigma_spatial)), 75 color_normalization_(1.0f / (2.0f * sigma_color * sigma_color)) {} 76 ComputeBilateralWeightComputer77 __device__ inline float Compute(const float row_diff, const float col_diff, 78 const float color1, 79 const float color2) const { 80 const float spatial_dist_squared = 81 row_diff * row_diff + col_diff * col_diff; 82 const float color_dist = color1 - color2; 83 return exp(-spatial_dist_squared * spatial_normalization_ - 84 color_dist * color_dist * color_normalization_); 85 } 86 87 private: 88 const float spatial_normalization_; 89 const float color_normalization_; 90 }; 91 92 } // namespace mvs 93 } // namespace colmap 94 95 #endif // COLMAP_SRC_MVS_GPU_MAT_REF_IMAGE_H_ 96