Feature learning for RGB-D scene understanding

Scene understanding is an important and fundamental problem in computer vision and is critical in applications of robotics and augmented reality. Scene understanding includes many tasks such as scene labeling, object recognition and scene classification. Most previous scene understanding methods focus on outdoor scenes. In contrast, indoor scene understanding is more challenging, due to poor illumination and cluttered objects. With the wide availability of affordable RGB-D cameras such as Kinect, huge changes have been made to indoor scene analysis due to the rich 3D geometry information provided by depth measurements. Feature extraction is the key part for scene understanding tasks. Most of the early methods extract hand-crafted features. However, the performance of such feature extractors highly depends on variations in hand-crafting and combinations. The designing process requires empirical understanding of data, thus hard to systematically extend to different modalities. In addition, the hand-crafted features usually capture a subset of recognition cues from raw data, which might ignore some useful information. Thus, in this research, we focus on feature learning with raw data as input. Particularly, we explore feature learning on three different tasks of indoor scene understanding using RGB-D input: • Scene labeling: The aim is to densely assign a category label (e.g. table, TV) to each pixel in an image. Inspired by the success of unsupervised feature learning, we start by adapting the existing unsupervised feature learning technique to directly learn features from RGB-D images. Typically, better performance could be achieved by further applying feature encoding over the learned features to build