HeIght gradient histogram (HIGH) for 3D scene labeling

RGB-D (color + 3D pointcloud) based scene labeling has received much attention due to the affordable RGB-D sensors such as Microsoft Kinect. To fully utilize the RGB-D data, it is critical to develop robust features that can reliably describe the 3D shape information of the pointcloud data. Previous work has proposed to extract SIFT-like features from the depth dimension data directly while ignored the important height dimension data of the 3D pointcloud. In this paper, we propose to describe 3D scene using height gradient information and propose a new compact pointcloud feature called HeIght Gradient Histogram (HIGH). Using TextonBoost as the pixel classifier, the experiments on two benchmarked 3D scene labeling datasets show that HIGH feature can well handle the intra-category variations of object class, and significantly improve class-average accuracy compared with the state-of-the-art results. We will publish the code of HIGH feature for the community.