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
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 --- 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)