scannet v2 数据集下载

scannet数据集：
一共1513个采集场景数据（每个场景中点云数量都不一样，如果要用到端到端可能需要采样，使每一个场景的点都相同），共21个类别的对象，其中，1201个场景用于训练，312个场景用于测试，有四个评测任务：3D语义分割、3D实例分割、2D语义分割和2D实例分割。

如果去官网下载，要填一个TOS协议，然后发邮件过去，会得到python脚本。
类似下面这样，脚本放在github里保存。

#coding:utf-8
#!/usr/bin/env python
# Downloads ScanNet public data release
# Run with ./download-scannet.py (or python download-scannet.py on Windows)
# -*- coding: utf-8 -*-
import argparse
import os
import urllib.request      #(for python3)
# import urllib
import tempfile

BASE_URL = 'http://kaldir.vc.in.tum.de/scannet/'
TOS_URL = BASE_URL + 'ScanNet_TOS.pdf'
FILETYPES = ['.sens', '.txt',
             '_vh_clean.ply', '_vh_clean_2.ply',
             '_vh_clean.segs.json', '_vh_clean_2.0.010000.segs.json',
             '.aggregation.json', '_vh_clean.aggregation.json',
             '_vh_clean_2.labels.ply',
             '_2d-instance.zip', '_2d-instance-filt.zip',
             '_2d-label.zip', '_2d-label-filt.zip']
FILETYPES_TEST = ['.sens', '.txt', '_vh_clean.ply', '_vh_clean_2.ply']
PREPROCESSED_FRAMES_FILE = ['scannet_frames_25k.zip', '5.6GB']
TEST_FRAMES_FILE = ['scannet_frames_test.zip', '610MB']
LABEL_MAP_FILES = ['scannetv2-labels.combined.tsv', 'scannet-labels.combined.tsv']
RELEASES = ['v2/scans', 'v1/scans']
RELEASES_TASKS = ['v2/tasks', 'v1/tasks']
RELEASES_NAMES = ['v2', 'v1']
RELEASE = RELEASES[0]
RELEASE_TASKS = RELEASES_TASKS[0]
RELEASE_NAME = RELEASES_NAMES[0]
LABEL_MAP_FILE = LABEL_MAP_FILES[0]
RELEASE_SIZE = '1.2TB'
V1_IDX = 1

### 剩下的就不贴了，你懂得

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36

整个数据大小是1.2T，太大了，只下载需要的部分。

python3 download-scannetv2.py -o scannet/ --type  _vh_clean_2.ply
python3 download-scannetv2.py -o scannet/ --type  _vh_clean_2.labels.ply
python3 download-scannetv2.py -o scannet/ --type  _vh_clean_2.0.010000.segs.json
python3 download-scannetv2.py -o scannet/ --type  .aggregation.json
1
2
3
4

下载好之后会是这样子的，
（有小伙伴想让把这几个文件夹发过去的，注意一下，这几个文件夹有20多G，邮件发是不现实的，
实在有想要的可以留言。）

显示其中一个看下

>>> import open3d as o3d
>>> pcd = o3d.io.read_point_cloud('scene0000_00_vh_clean_2.ply')
>>> o3d.visualization.draw_geometries([pcd])
1
2
3

看下它的语义标签

>>> pcd = o3d.io.read_point_cloud('scene0000_00_vh_clean_2.labels.ply')
>>> o3d.visualization.draw_geometries([pcd])
1
2