３次元再構成，３次元セグメンテーション（書きかけ）

前準備: Python, Open3D のインストール: 別ページ »で説明している．

https://github.com/isl-org/Open3D-ML に記載の次のプログラムを実行

import open3d.ml.tf as ml3d

# construct a dataset by specifying dataset_path
dataset = ml3d.datasets.SemanticKITTI(dataset_path='/path/to/SemanticKITTI/')

# get the 'all' split that combines training, validation and test set
all_split = dataset.get_split('all')

# print the attributes of the first datum
print(all_split.get_attr(0))

# print the shape of the first point cloud
print(all_split.get_data(0)['point'].shape)

# show the first 100 frames using the visualizer
vis = ml3d.vis.Visualizer()
vis.visualize_dataset(dataset, 'all', indices=range(100))

py -3.8 -m pip install open3
ScanNet

ScanNet is an RGB-D video dataset containing 2.5 million views in more than 1500 scans, annotated with 3D camera poses, surface reconstructions, and instance-level semantic segmentations.

https://github.com/ScanNet/ScanNet

http://www.scan-net.org

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SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite

https://rgbd.cs.princeton.edu/

https://rgbd.cs.princeton.edu/data/SUNRGBD.zip

The dataset contains RGB-D images from NYU depth v2 [1], Berkeley B3DO [2], and SUN3D [3]. Besides this paper, you are required to also cite the following papers if you use this dataset. 
 [1] N. Silberman, D. Hoiem, P. Kohli, R. Fergus. Indoor segmentation and support inference from rgbd images. In ECCV, 2012.

[2] A. Janoch, S. Karayev, Y. Jia, J. T. Barron, M. Fritz, K. Saenko, and T. Darrell. A category-level 3-d object dataset: Putting the kinect to work. In ICCV Workshop on Consumer Depth Cameras for Computer Vision, 2011.

[3] J. Xiao, A. Owens, and A. Torralba. SUN3D: A database of big spaces reconstructed using SfM and object labels. In ICCV, 2013 

S. Song, S. Lichtenberg, and J. Xiao.
SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite
Proceedings of 28th IEEE Conference on Computer Vision and Pattern Recognition (CVPR2015)