Boosting Object Recognition in Point Clouds by Saliency Detection

Object recognition in 3D point clouds is a challenging task, mainly when time is an important factor to deal with, such as in industrial applications. Local descriptors are an amenable choice whenever the 6 DoF pose of recognized objects should also be estimated. However, the pipeline for this kind of descriptors is highly time-consuming. In this work, we propose an update to the traditional pipeline, by adding a preliminary filtering stage referred to as saliency boost. We perform tests on a standard object recognition benchmark by considering four keypoint detectors and four local descriptors, in order to compare time and recognition performance between the traditional pipeline and the boosted one. Results on time show that the boosted pipeline could turn out up to 5 times faster, with the recognition rate improving in most of the cases and exhibiting only a slight decrease in the others. These results suggest that the boosted pipeline can speed-up processing time substantially with limited impacts or even benefits in recognition accuracy.

[1]  Federico Tombari,et al.  Performance Evaluation of 3D Keypoint Detectors , 2011, 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission.

[2]  Dan Su,et al.  Multi-modal fusion network with multi-scale multi-path and cross-modal interactions for RGB-D salient object detection , 2019, Pattern Recognit..

[3]  Rafael Beserra Gomes,et al.  Efficient 3D object recognition using foveated point clouds , 2013, Comput. Graph..

[4]  Leslie G. Ungerleider,et al.  Mechanisms of visual attention in the human cortex. , 2000, Annual review of neuroscience.

[5]  Tao Wang,et al.  Saliency ranker: A new salient object detection method , 2018, J. Vis. Commun. Image Represent..

[6]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[7]  Radu Bogdan Rusu,et al.  3D is here: Point Cloud Library (PCL) , 2011, 2011 IEEE International Conference on Robotics and Automation.

[8]  Nico Blodow,et al.  Aligning point cloud views using persistent feature histograms , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

[10]  Alexandros Iosifidis,et al.  Probabilistic saliency estimation , 2016, Pattern Recognit..

[11]  Nico Blodow,et al.  Fast Point Feature Histograms (FPFH) for 3D registration , 2009, 2009 IEEE International Conference on Robotics and Automation.

[12]  Zhuowen Tu,et al.  Deeply Supervised Salient Object Detection with Short Connections , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Mohammed Bennamoun,et al.  A Comprehensive Performance Evaluation of 3D Local Feature Descriptors , 2015, International Journal of Computer Vision.

[14]  Jianxiong Xiao,et al.  Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Yu Zhong,et al.  Intrinsic shape signatures: A shape descriptor for 3D object recognition , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[16]  Luís A. Alexandre.  3D Descriptors for Object and Category Recognition: a Comparative Evaluation , 2012 .

[17]  Andrew E. Johnson,et al.  Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Federico Tombari,et al.  SHOT: Unique signatures of histograms for surface and texture description , 2014, Comput. Vis. Image Underst..

[19]  Hui Chen,et al.  3D free-form object recognition in range images using local surface patches , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..