Matterport3D，简称MP3D，是室内场景的一个大规模 RGB-D 数据集。可用于机器人具身导航算法开发。
包含 90 个建筑规模场景的 194,400 张RGB-D图像，以及10,800个全景视图。
数据集提供了表面重建、相机姿态以及二维和三维语义分割的注释。
支持各种监督和自监督计算机视觉任务，包括关键点匹配、视图重叠预测、基于颜色的法线预测、语义分割和场景分类。
项目地址：https://niessner.github.io/Matterport/
代码地址：https://github.com/niessner/Matterport
论文地址：Matterport3D: Learning from RGB-D Data in Indoor Environments
1、数据集下载（官方）
该数据集包含多种类型的标注：彩色和深度图像、相机姿态、带纹理的 3D 网格、建筑平面图和区域标注、对象实例语义标注。
数据集的格式参考：https://github.com/niessner/Matterport/blob/master/data_organization.md
需要填写并签署使用条款协议表格，然后将其发送至matterport3d@googlegroups.com以请求访问数据集。
2、Habitat-Lab 提供示例下载
Scenes datasets 如下表格所示：
| Scenes models | Extract path | Archive size |
|---|---|---|
| Habitat test scenes | data/scene_datasets/habitat-test-scenes/{scene}.glb |
89 MB |
| ReplicaCAD | data/scene_datasets/replica_cad/configs/scenes/{scene}.scene_instance.json |
123 MB |
| HM3D | data/scene_datasets/hm3d/{split}/00\d\d\d-{scene}/{scene}.basis.glb |
130 GB |
| Gibson | data/scene_datasets/gibson/{scene}.glb |
1.5 GB |
| MatterPort3D | data/scene_datasets/mp3d/{scene}/{scene}.glb |
15 GB |
| HSSD-Habitat | data/scene_datasets/hssd-hab/scenes/{scene}.scene_instance.json |
XXXXX GB |
| AI2-THOR-Habitat | data/scene_datasets/ai2thor-hab/ai2thor-hab/configs/scenes/{DATASET}/{scene}.scene_instance.json |
XXXXX GB |
点击“Matterport3D”后，能看到下载命令：
python -m habitat_sim.utils.datasets_download --uids mp3d_example_scene --data-path data/但是这只是一个示例场景，用于在 Habitat-sim 中执行单元测试，无法下载完整的“Matterport3D”数据。
Task datasets 如下表格所示：
3、批量下载
首先创建一个Conda环境，名字为mp3d，python版本为3.9
进入mp3d环境
conda create -n mp3d python=3.9
conda activate mp3d然后安装requests依赖库
pip install requests编写批量下载的代码：
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import argparse
import collections
import os
import tempfile
import urllib
from urllib import request
import requests
from time import sleep
from requests.exceptions import ConnectionError, ChunkedEncodingError, RequestException
import sys
BASE_URL = 'http://kaldir.vc.in.tum.de/matterport/'
RELEASE = 'v1/scans'
RELEASE_TASKS = 'v1/tasks/'
RELEASE_SIZE = '1.3TB'
TOS_URL = BASE_URL + 'MP_TOS.pdf'
FILETYPES = [
'cameras',
'matterport_camera_intrinsics',
'matterport_camera_poses',
'matterport_color_images',
'matterport_depth_images',
'matterport_hdr_images',
'matterport_mesh',
'matterport_skybox_images',
'undistorted_camera_parameters',
'undistorted_color_images',
'undistorted_depth_images',
'undistorted_normal_images',
'house_segmentations',
'region_segmentations',
'image_overlap_data',
'poisson_meshes',
'sens'
]
TASK_FILES = {
'keypoint_matching_data': ['keypoint_matching/data.zip'],
'keypoint_matching_models': ['keypoint_matching/models.zip'],
'surface_normal_data': ['surface_normal/data_list.zip'],
'surface_normal_models': ['surface_normal/models.zip'],
'region_classification_data': ['region_classification/data.zip'],
'region_classification_models': ['region_classification/models.zip'],
'semantic_voxel_label_data': ['semantic_voxel_label/data.zip'],
'semantic_voxel_label_models': ['semantic_voxel_label/models.zip'],
'minos': ['mp3d_minos.zip'],
'gibson': ['mp3d_for_gibson.tar.gz'],
'habitat': ['mp3d_habitat.zip'],
'pixelsynth': ['mp3d_pixelsynth.zip'],
'igibson': ['mp3d_for_igibson.zip'],
'mp360': ['mp3d_360/data_00.zip', 'mp3d_360/data_01.zip', 'mp3d_360/data_02.zip', 'mp3d_360/data_03.zip', 'mp3d_360/data_04.zip', 'mp3d_360/data_05.zip', 'mp3d_360/data_06.zip']
}
def get_release_scans(release_file):
# scan_lines = urllib.urlopen(release_file)
scan_lines = request.urlopen(release_file)
scans = []
for scan_line in scan_lines:
scan_line = str(scan_line, 'utf-8')
scan_id = scan_line.rstrip('\n')
scans.append(scan_id)
return scans
def download_release(release_scans, out_dir, file_types):
print('Downloading MP release to ' + out_dir + '...')
for scan_id in release_scans:
scan_out_dir = os.path.join(out_dir, scan_id)
download_scan(scan_id, scan_out_dir, file_types)
print('Downloaded MP release.')
def download_file(url, out_file, max_retries=5, chunk_size=1024*1024):
"""
下载单个文件，支持断点续传和自动重试，并打印实时下载进度。
兼容 Python2/3。
"""
out_dir = os.path.dirname(out_file)
if not os.path.isdir(out_dir):
try:
os.makedirs(out_dir)
except OSError:
pass
# 获取文件总大小
head = requests.head(url, allow_redirects=True)
if head.status_code != 200:
raise IOError("无法获取文件大小: {0}，状态码 {1}".format(url, head.status_code))
total_size = int(head.headers.get('Content-Length', 0))
# 计算已下载起点
resume = 0
if os.path.exists(out_file):
resume = os.path.getsize(out_file)
if resume >= total_size:
print("跳过已存在文件 %s" % out_file)
return
print("开始下载 %s (%0.2f MB)" % (out_file, total_size / 1024.0**2))
retries = 0
last_print = 0
while resume < total_size and retries <= max_retries:
headers = {"Range": "bytes=%d-%d" % (resume, total_size - 1)}
try:
r = requests.get(url, headers=headers, stream=True, timeout=30)
r.raise_for_status()
fh, tmp_path = tempfile.mkstemp(dir=out_dir)
with os.fdopen(fh, "wb") as tmpf:
for chunk in r.iter_content(chunk_size=chunk_size):
if not chunk:
continue
tmpf.write(chunk)
resume += len(chunk)
# 每下载 5% 或最后一块时打印一次进度
percent = int(resume * 100 / total_size)
if percent - last_print >= 5 or resume == total_size:
sys.stdout.write("\r下载进度: %3d%% (%0.2f/%0.2f MB)" % (
percent,
resume / 1024.0**2,
total_size / 1024.0**2
))
sys.stdout.flush()
last_print = percent
# 将本次下载数据追加到目标文件
with open(tmp_path, "rb") as tmpf, open(out_file, "ab") as outf:
outf.write(tmpf.read())
os.remove(tmp_path)
r.close()
break
# except (requests.ConnectionError, requests.ChunkedEncodingError, IOError) as e:
except (ConnectionError, ChunkedEncodingError, IOError) as e:
retries += 1
wait = 2 ** retries
print("\n[重试 %d/%d] 已下载 %0.2f MB，等待 %d 秒后重试..." % (
retries, max_retries,
resume / 1024.0**2,
wait
))
sleep(wait)
if resume < total_size:
raise IOError("下载失败：只获取到 %d / %d 字节" % (resume, total_size))
# 下载完成后换行
sys.stdout.write("\n下载完成：%s\n" % out_file)
def download_scan(scan_id, out_dir, file_types):
print('Downloading MP scan ' + scan_id + ' ...')
if not os.path.isdir(out_dir):
os.makedirs(out_dir)
for ft in file_types:
url = BASE_URL + RELEASE + '/' + scan_id + '/' + ft + '.zip'
out_file = out_dir + '/' + ft + '.zip'
download_file(url, out_file)
print('Downloaded scan ' + scan_id)
def download_task_data(task_data, out_dir):
print('Downloading MP task data for ' + str(task_data) + ' ...')
for task_data_id in task_data:
if task_data_id in TASK_FILES:
file = TASK_FILES[task_data_id]
for filepart in file:
url = BASE_URL + RELEASE_TASKS + '/' + filepart
localpath = os.path.join(out_dir, filepart)
localdir = os.path.dirname(localpath)
if not os.path.isdir(localdir):
os.makedirs(localdir)
download_file(url, localpath)
print('Downloaded task data ' + task_data_id)
def main():
parser = argparse.ArgumentParser(description=
'''
Downloads MP public data release.
Example invocation:
python download_mp.py -o base_dir --id ALL --type object_segmentations --task_data semantic_voxel_label_data semantic_voxel_label_models
The -o argument is required and specifies the base_dir local directory.
After download base_dir/v1/scans is populated with scan data, and base_dir/v1/tasks is populated with task data.
Unzip scan files from base_dir/v1/scans and task files from base_dir/v1/tasks/task_name.
The --type argument is optional (all data types are downloaded if unspecified).
The --id ALL argument will download all house data. Use --id house_id to download specific house data.
The --task_data argument is optional and will download task data and model files.
''',
formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument('-o', '--out_dir', required=True, help='directory in which to download')
parser.add_argument('--task_data', default=[], nargs='+', help='task data files to download. Any of: ' + ','.join(TASK_FILES.keys()))
parser.add_argument('--id', default='ALL', help='specific scan id to download or ALL to download entire dataset')
parser.add_argument('--type', nargs='+', help='specific file types to download. Any of: ' + ','.join(FILETYPES))
args = parser.parse_args()
release_file = BASE_URL + RELEASE + '.txt'
release_scans = get_release_scans(release_file)
file_types = FILETYPES
# download task data
if args.task_data:
if set(args.task_data) & set(TASK_FILES.keys()): # download task data
out_dir = os.path.join(args.out_dir, RELEASE_TASKS)
download_task_data(args.task_data, out_dir)
else:
print('ERROR: Unrecognized task data id: ' + args.task_data)
print('Done downloading task_data for ' + str(args.task_data))
# key = raw_input('Press any key to continue on to main dataset download, or CTRL-C to exit.')
key = input('Press any key to continue on to main dataset download, or CTRL-C to exit.')
# download specific file types?
if args.type:
if not set(args.type) & set(FILETYPES):
# print('ERROR: Invalid file type: ' + file_type)
print('ERROR: Invalid file type: ' + file_types)
return
file_types = args.type
if args.id and args.id != 'ALL': # download single scan
scan_id = args.id
if scan_id not in release_scans:
print('ERROR: Invalid scan id: ' + scan_id)
else:
out_dir = os.path.join(args.out_dir, RELEASE, scan_id)
download_scan(scan_id, out_dir, file_types)
elif 'minos' not in args.task_data and args.id == 'ALL' or args.id == 'all': # download entire release
if len(file_types) == len(FILETYPES):
print('WARNING: You are downloading the entire MP release which requires ' + RELEASE_SIZE + ' of space.')
else:
print('WARNING: You are downloading all MP scans of type ' + file_types[0])
print('Note that existing scan directories will be skipped. Delete partially downloaded directories to re-download.')
print('***')
print('Press any key to continue, or CTRL-C to exit.')
# key = raw_input('')
key = input('')
out_dir = os.path.join(args.out_dir, RELEASE)
download_release(release_scans, out_dir, file_types)
if __name__ == "__main__": main()
创建一个文件夹，用于存放数据集，比如data
下载所以MP3D的数据，1.3T左右，执行命令：
python download_mp.py --task habitat -o ./data打印信息：
Downloading MP task data for ['habitat'] ...
Done downloading task_data for ['habitat']
Press any key to continue on to main dataset download, or CTRL-C to exit.
WARNING: You are downloading the entire MP release which requires 1.3TB of space.
Note that existing scan directories will be skipped. Delete partially downloaded directories to re-download.
***
Press any key to continue, or CTRL-C to exit.Downloading MP release to ./data/v1/scans...
Downloading MP scan 1pXnuDYAj8r ...
下载完成：./data/v1/scans/1pXnuDYAj8r/cameras.zip
开始下载 ./data/v1/scans/1pXnuDYAj8r/matterport_camera_intrinsics.zip (0.10 MB)
下载进度: 100% (0.10/0.10 MB)
下载完成：./data/v1/scans/1pXnuDYAj8r/matterport_camera_intrinsics.zip
开始下载 ./data/v1/scans/1pXnuDYAj8r/matterport_camera_poses.zip (0.61 MB)
下载进度: 100% (0.61/0.61 MB)
下载完成：./data/v1/scans/1pXnuDYAj8r/matterport_camera_poses.zip
开始下载 ./data/v1/scans/1pXnuDYAj8r/matterport_color_images.zip (537.00 MB)
下载进度: 100% (537.00/537.00 MB)
............
下载完成：./data/v1/scans/1pXnuDYAj8r/undistorted_color_images.zip
开始下载 ./data/v1/scans/1pXnuDYAj8r/undistorted_depth_images.zip (1282.63 MB)
下载进度: 100% (1282.63/1282.63 MB)
下载完成：./data/v1/scans/1pXnuDYAj8r/undistorted_depth_images.zip
开始下载 ./data/v1/scans/1pXnuDYAj8r/undistorted_normal_images.zip (2808.76 MB)
下载进度: 90% (2528.00/2808.76 MB)
下载过程中，场景数据的文件：
17DRP5sb8fy任务包含的内容：
...............
任务数据文件：
指定某个场景ID，进行下载，执行命令：
python download_mp.py --task habitat -o ./data --id 17DRP5sb8fy打印信息：
Downloading MP scan 17DRP5sb8fy ...
开始下载 ./data/v1/scans/17DRP5sb8fy/cameras.zip (0.00 MB)
下载进度: 100% (0.00/0.00 MB)
下载完成：./data/v1/scans/17DRP5sb8fy/cameras.zip
开始下载 ./data/v1/scans/17DRP5sb8fy/matterport_camera_intrinsics.zip (0.05 MB)
下载进度: 100% (0.05/0.05 MB)
........
下载完成：./data/v1/scans/17DRP5sb8fy/image_overlap_data.zip
开始下载 ./data/v1/scans/17DRP5sb8fy/poisson_meshes.zip (136.50 MB)
下载进度: 100% (136.50/136.50 MB)
下载完成：./data/v1/scans/17DRP5sb8fy/poisson_meshes.zip
开始下载 ./data/v1/scans/17DRP5sb8fy/sens.zip (1402.69 MB)
下载进度: 100% (1402.69/1402.69 MB)
下载完成：./data/v1/scans/17DRP5sb8fy/sens.zip
Downloaded scan 17DRP5sb8fy
分享完成～