Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.neucom.2015.10.005 | Neurocomputing |
Keywords | Field | DocType |
Point cloud,Labeling,Object detection,RGB-D | Computer vision,Object detection,Viola–Jones object detection framework,Pattern recognition,Robustness (computer science),Pixel,RGB color model,Artificial intelligence,Parsing,Point cloud,Mathematics,3D reconstruction | Journal |
Volume | ISSN | Citations |
174 | 0925-2312 | 3 |
PageRank | References | Authors |
0.37 | 33 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Liu | 1 | 4041 | 204.19 |
| Shaozi Li | 2 | 403 | 54.27 |
| Cao Donglin | 3 | 25 | 4.06 |
| Song-zhi Su | 4 | 61 | 8.53 |
| Rongrong Ji | 5 | 3616 | 189.98 |