https://niessner.github.io/Matterport/

Matterport3D is a pretty awesome dataset for RGB-D machine learning tasks :)
https://niessner.github.io/Matterport/

3d-reconstruction semantic-scene-understanding

Last synced: 3 months ago
JSON representation

Matterport3D is a pretty awesome dataset for RGB-D machine learning tasks :)

• Host: GitHub
• URL: https://niessner.github.io/Matterport/
• Owner: niessner
• License: mit
• Created: 2016-12-15T20:35:13.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2023-11-14T15:45:50.000Z (7 months ago)
• Last Synced: 2024-01-16T10:33:14.616Z (5 months ago)
• Topics: 3d-reconstruction, semantic-scene-understanding
• Language: C++
• Homepage: https://niessner.github.io/Matterport/
• Size: 29.4 MB
• Stars: 859
• Watchers: 41
• Forks: 154
• Open Issues: 49
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

Lists

• awesome-point-cloud-analysis - [Matterport3D - D: 10,800 panoramic views from 194,400 RGB-D images. Annotations: surface reconstructions, camera poses, and 2D and 3D semantic segmentations. Keypoint matching, view overlap prediction, normal prediction from color, semantic segmentation, and scene classification. [[3DV 2017 paper](https://arxiv.org/abs/1709.06158)] [[code](https://github.com/niessner/Matterport)] [[blog](https://matterport.com/blog/2017/09/20/announcing-matterport3d-research-dataset/)] (2021)
• awesome-point-cloud-analysis-2023 - [Matterport3D - D: 10,800 panoramic views from 194,400 RGB-D images. Annotations: surface reconstructions, camera poses, and 2D and 3D semantic segmentations. Keypoint matching, view overlap prediction, normal prediction from color, semantic segmentation, and scene classification. [[3DV 2017 paper](https://arxiv.org/abs/1709.06158)] [[code](https://github.com/niessner/Matterport)] [[blog](https://matterport.com/blog/2017/09/20/announcing-matterport3d-research-dataset/)] (2023)
• awesome_pointcloud_process - [Matterport3D - D: 10,800 panoramic views from 194,400 RGB-D images. Annotations: surface reconstructions, camera poses, and 2D and 3D semantic segmentations. Keypoint matching, view overlap prediction, normal prediction from color, semantic segmentation, and scene classification. [[3DV 2017 paper](https://arxiv.org/abs/1709.06158)] [[code](https://github.com/niessner/Matterport)] [[blog](https://matterport.com/blog/2017/09/20/announcing-matterport3d-research-dataset/)] (点云数据集 / 商用)
• awesome-point-cloud-analysis - [Matterport3D - D: 10,800 panoramic views from 194,400 RGB-D images. Annotations: surface reconstructions, camera poses, and 2D and 3D semantic segmentations. Keypoint matching, view overlap prediction, normal prediction from color, semantic segmentation, and scene classification. [[3DV 2017 paper](https://arxiv.org/abs/1709.06158)] [[code](https://github.com/niessner/Matterport)] [[blog](https://matterport.com/blog/2017/09/20/announcing-matterport3d-research-dataset/)] (2020)
• awesome-rgbd-datasets - MatterPort3D - voxel segmentation |90 scenes, 10800 panoramic views (194400 images) |2017 | (RGB-D Datasets <a id="list" class="anchor" href="#list" aria-hidden="true"><span class="octicon octicon-link"></span></a>)
• awesome-3D-vision - [Matterport3D - D: 10,800 panoramic views from 194,400 RGB-D images. Annotations: surface reconstructions, camera poses, and 2D and 3D semantic segmentations. Keypoint matching, view overlap prediction, normal prediction from color, semantic segmentation, and scene classification. [[3DV 2017 paper](https://arxiv.org/abs/1709.06158)] [[code](https://github.com/niessner/Matterport)] [[blog](https://matterport.com/blog/2017/09/20/announcing-matterport3d-research-dataset/)] (点云数据集 / 商用)
• awesome-pointcloud-processing - [Matterport3D - D: 10,800 panoramic views from 194,400 RGB-D images. Annotations: surface reconstructions, camera poses, and 2D and 3D semantic segmentations. Keypoint matching, view overlap prediction, normal prediction from color, semantic segmentation, and scene classification. [[3DV 2017 paper](https://arxiv.org/abs/1709.06158)] [[code](https://github.com/niessner/Matterport)] [[blog](https://matterport.com/blog/2017/09/20/announcing-matterport3d-research-dataset/)] (点云数据集 / 商用)