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Datasets/3DMatch

3DMatch

Various

The 3DMATCH benchmark evaluates how well descriptors (both 2D and 3D) can establish correspondences between RGB-D frames of different views. The dataset contains 2D RGB-D patches and 3D patches (local TDF voxel grid volumes) of wide-baselined correspondences.

The pixel size of each 2D patch is determined by the projection of the 0.3m3 local 3D patch around the interest point onto the image plane.

Source: 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions

Related Benchmarks

3DMatch (at least 30% overlapped - FCGF setting)/3D Point Cloud Interpolation/RE (all)3DMatch (at least 30% overlapped - FCGF setting)/3D Point Cloud Interpolation/Recall (0.3m, 15 degrees)3DMatch (at least 30% overlapped - FCGF setting)/3D Point Cloud Interpolation/TE (all)3DMatch (at least 30% overlapped - FCGF setting)/Point Cloud Registration/RE (all)3DMatch (at least 30% overlapped - FCGF setting)/Point Cloud Registration/Recall (0.3m, 15 degrees)3DMatch (at least 30% overlapped - FCGF setting)/Point Cloud Registration/TE (all)3DMatch (at least 30% overlapped - sample 5k interest points)/3D Point Cloud Interpolation/Recall ( correspondence RMSE below 0.2)3DMatch (at least 30% overlapped - sample 5k interest points)/Point Cloud Registration/Recall ( correspondence RMSE below 0.2)3DMatch (trained on KITTI)/3D Point Cloud Interpolation/Recall3DMatch (trained on KITTI)/Point Cloud Registration/Recall3DMatch Benchmark/3D/Average Recall3DMatch Benchmark/3D Point Cloud Interpolation/Feature Matching Recall3DMatch Benchmark/Image Enhancement/mAP (@0.1, Through-wall)3DMatch Benchmark/Point Cloud Registration/Feature Matching Recall

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Papers
175
Benchmarks
0

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Tasks

3D Feature MatchingLow-Light Image EnhancementPoint Cloud Registration