Access to large, diverse RGB-D datasets is critical for training RGB-D scene
understanding algorithms. However, existing datasets still cover only a limited
number of views or a restricted scale of spaces. In this paper, we introduce
Matterport3D, a large-scale RGB-D dataset containing 10,800 panoramic views
from 194,400 RGB-D images of 90 building-scale scenes. Annotations are provided
with surface reconstructions, camera poses, and 2D and 3D semantic
segmentations. The precise global alignment and comprehensive, diverse
panoramic set of views over entire buildings enable a variety of supervised and
self-supervised computer vision tasks, including keypoint matching, view
overlap prediction, normal prediction from color, semantic segmentation, and
region classification.

本文介绍 Matterport3D 数据集，包含 10,800 个全景视图，通过 RGB-D 图片提供了 90 个建筑尺度的场景的表面重建、相机姿态和 2D、3D 语义分割注释等，可用于多种计算机视觉任务，如关键点匹配、视图重叠预测、颜色预测等。

Matterport3D: 室内环境下 RGB-D 数据的学习

Matterport3D: Learning from RGB-D Data in Indoor Environments

Location recognition is commonly treated as visual instance retrieval on
"street view" imagery. The dataset items and queries are panoramic views, i.e.
groups of images taken at a single location. This work introduces a novel
panorama-to-panorama matching process, either by aggregating features of
individual images in a group or by explicitly constructing a larger panorama.
In either case, multiple views are used as queries. We reach near perfect
location recognition on a standard benchmark with only four query views.

本文研究如何通过聚合图像组中各个图像的特征或构造更大的全景图从而实现全景图之间的匹配识别，有效地提高了基于多个查询视图的标准基准的地点识别性能。