Unsupervised object region proposals for RGB-D indoor scenes

Abstract

In this paper, we present a novel unsupervised framework for automatically generating bottom up class independent object candidates for detection and recognition in cluttered indoor environments. Utilizing raw depth map from active sensors such as Kinect, we propose a novel plane segmentation algorithm for dividing an indoor scene into predominant planar regions and non-planar regions. Based on this partition, we are able to effectively predict object locations and their spatial extensions. Our approach automatically generates object proposals considering five different aspects: Non-planar Regions (NPR), Planar Regions (PR), Detected Planes (DP), Merged Detected Planes (MDP) and Hierarchical Clustering (HC) of 3D point clouds. Object region proposals include both bounding boxes and instance segments. Our approach achieves very competitive results and is even able to outperform supervised state-of-the-art algorithms on the challenging NYU-v2 RGB-Depth dataset. In addition, we apply our approach to the most recently released large scale RGB-Depth dataset from Princeton University – “SUN RGBD”, which utilizes four different depth sensors. Its consistent performance demonstrates a general applicability of our approach.