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INRIA Lafarge Benchmarks
2D Box
Fusion Box
Person
|Urban
|Autonomous Driving
|...
许可协议: Unknown

Overview

Some datasets and evaluation tools are provided on this page for four different computer vision and computer graphics problems.Population countingLine-network extraction3D-object detectionSurface reconstructionSurface reconstructionWe focus here on the well-known surface reconstruction problem from point clouds. The datasets include simple synthetic objects, but also real-world urban scenes containing various types of geometric structures. The file format for both point clouds and triangular meshes is ply. The software Meshlab can be used to read and convert these files.Evaluation of the results: The results are evaluated according to the error to the ground truth and/or the input point cloud (Hausdorff distance), and time. Meshlab can be used to compute the Hausdorff distance between your results and the given input point clouds / reference meshes.Smooth_cube (100K points)Distorted_cube (50K points)Maya_temple (2M points)Maya_temple_cor (0.83M points)Empire_state_building_cor (1.2M points)Lans_le_Villard (1.23M points)Cathedral_facade (1.45M points)3D-object detectionThe goal consists in detecting a specific class of 3D-objects from point clouds. The file format for point clouds is ply. The software Meshlab can be used to read and convert the files.Evaluation of the results: The results can be evaluated according to criteria: the estimated number of objects, and the precision of their position. You can use this Matlab code to evaluate your results from the following datasets.Urban_small (140K points)Urban (2.27M points)

数据概要
数据格式
point cloud, image,
数据量
--
文件大小
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| 数据量 -- | 大小 --
INRIA Lafarge Benchmarks
2D Box Fusion Box
Person | Urban | Autonomous Driving
许可协议: Unknown

Overview

Some datasets and evaluation tools are provided on this page for four different computer vision and computer graphics problems.Population countingLine-network extraction3D-object detectionSurface reconstructionSurface reconstructionWe focus here on the well-known surface reconstruction problem from point clouds. The datasets include simple synthetic objects, but also real-world urban scenes containing various types of geometric structures. The file format for both point clouds and triangular meshes is ply. The software Meshlab can be used to read and convert these files.Evaluation of the results: The results are evaluated according to the error to the ground truth and/or the input point cloud (Hausdorff distance), and time. Meshlab can be used to compute the Hausdorff distance between your results and the given input point clouds / reference meshes.Smooth_cube (100K points)Distorted_cube (50K points)Maya_temple (2M points)Maya_temple_cor (0.83M points)Empire_state_building_cor (1.2M points)Lans_le_Villard (1.23M points)Cathedral_facade (1.45M points)3D-object detectionThe goal consists in detecting a specific class of 3D-objects from point clouds. The file format for point clouds is ply. The software Meshlab can be used to read and convert the files.Evaluation of the results: The results can be evaluated according to criteria: the estimated number of objects, and the precision of their position. You can use this Matlab code to evaluate your results from the following datasets.Urban_small (140K points)Urban (2.27M points)

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