Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
411643 | Neurocomputing | 2016 | 6 Pages |
While much exciting progress is being made in 3D reconstruction of scenes, object labeling of 3D point cloud for indoor scenes has been left as a challenge issue. How should we explore the reference images of 3D scene, in aid of scene parsing? In this paper, we propose a framework for 3D indoor scenes labeling, based upon object detection on the RGB-D frames of 3D scene. First, the point cloud is segmented into homogeneous segments. Then, we utilize object detectors to assign class probabilities to pixels in every RGB-D frame. After that, the class probabilities are projected into the segments. Finally, we perform accurate inference on a MRF model over the homogeneous segments, in combination with geometry cues to output the labels. Experiment on the challenging RGB-D Object Dataset demonstrates that our detection based approach produces accurate labeling and improves the robustness of small object detection for indoor scenes.