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ICCV2019《KPConv: Flexible and Deformable Convolution for Point Clouds》

时间:2019-11-25 23:38:09      阅读:207      评论:0      收藏:0      [点我收藏+]

标签:ann   ict   ndt   net   can   man   save   uil   back   

针对semantic数据集:

  • 1.数据集准备:

Semantic3D dataset can be found <a href="http://www.semantic3d.net/view_dbase.php?chl=2">here</a>. Download and unzip every point cloud as ascii files and place them in a folder called `Data/Semantic3D/original_data`. You also have to download and unzip the groundthruth labels as ascii files in the same folder.

 

        # Dict from labels to names
        self.label_to_names = {0: ‘unlabeled‘,
                               1: ‘man-made terrain‘,
                               2: ‘natural terrain‘,
                               3: ‘high vegetation‘,
                               4: ‘low vegetation‘,
                               5: ‘buildings‘,
                               6: ‘hard scape‘,
                               7: ‘scanning artefacts‘,
                               8: ‘cars‘}

 

  • 2.降采样以节约空间
                # Subsample to save space
                sub_points, sub_colors, sub_labels = grid_subsampling(points,
                                                                      features=colors,
                                                                      labels=labels,
                                                                      sampleDl=0.01)
  • 3.降采样后的点写入文件.ply文件,储存格式是:x,y,z,r,g,b,l.
                # Write the subsampled ply file
                write_ply(ply_file_full, (sub_points, sub_colors, sub_labels), [‘x‘, ‘y‘, ‘z‘, ‘red‘, ‘green‘, ‘blue‘, ‘class‘])

 

 

 

 

 

 

 

 

 

 

ICCV2019《KPConv: Flexible and Deformable Convolution for Point Clouds》

标签:ann   ict   ndt   net   can   man   save   uil   back   

原文地址:https://www.cnblogs.com/yibeimingyue/p/11931320.html

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