KTH-3D-TOTAL: A 3D dataset for discovering spatial structures for long-term autonomous learning

Abstract

Long-term autonomous learning of human environments entails modelling and generalizing over distinct variations in: object instances in different scenes, and different scenes with respect to space and time. It is crucial for the robot to recognize the structure and context in spatial arrangements and exploit these to learn models which capture the essence of these distinct variations. Table-tops posses a typical structure repeatedly seen in human environments and are identified by characteristics of being personal spaces of diverse functionalities and dynamically changing due to human interactions. In this paper, we present a 3D dataset of 20 office table-tops manually observed and scanned 3 times a day as regularly as possible over 19 days (461 scenes) and subsequently, manually annotated with 18 different object classes, including multiple instances. We analyse the dataset to discover spatial structures and patterns in their variations. The dataset can, for example, be used to study the spatial relations between objects and long-term environment models for applications such as activity recognition, context and functionality estimation and anomaly detection.