Matterport3D: Learning from RGB-D Data in Indoor Environments

TLDR

Large, diverse RGB‑D datasets are essential for training scene‑understanding algorithms, but current datasets are limited in views or scale. The paper introduces Matterport3D, a large‑scale RGB‑D dataset comprising 10,800 panoramic views from 194,400 images across 90 building‑scale scenes. The dataset includes surface reconstructions, camera poses, and 2D/3D semantic segmentations. Its precise global alignment and diverse panoramic views enable supervised and self‑supervised tasks such as keypoint matching, view overlap prediction, normal prediction from color, semantic segmentation, and region classification.

Abstract

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.