Estimation of volume of a solid object From Three Dimensional Point Clouds Generated By Convolutional Neural Networks based Semantic Segmentation

Radhamadhab Dalai*
* Department of Computer Science & Engineering,Birla Institute of Technology, Ranchi, India.
Periodicity:March - May'2019
DOI : https://doi.org/10.26634/jpr.6.1.16458

Abstract

Generating three dimensional point cloud for an object in image has found many applications in used in many computer vision systems. In this work a convolutional neural network based semantic segmentation has been used to find region of interest in an image. The region of interest has been represented as point clouds in three dimensional space. Then using image processing technique area based filter operations have been applied to find the total surface area. Finally adding all these small volumes total volume has been calculated. A large number of algorithms have been adapted reconstruction methods have been experimented and tested only for uniform backgrounds, which is disadvantageous for the applications on real images which consists of complex nonuniform regions. In this work semantic segmentation has been used to partition the regions into similar instance based regions. We have used UNET model for the region based segmentation. Then using encoderdecoder scheme the 3D point cloud has been generated after merging pixel clouds. This paper proposes an end-to-end efficient generation network, which is composed of an encoder, a 3D image model, and a decoder. First, a single-view image of object and a nearest-shape retrieval has been formed from UNET are fed into the network; then, the two encoders are merged adaptively according to their homo-graphic or similarity in nature. Then decoder generates fine-grained point clouds from the pixel clouds generated from multiple view images. Each point in the cloud represents a weight according the intensity and color information from which the density and volume of object has been calculated. The experiments on uniform background images show that our method attains accuracy 12 to 15 %margin compared with volumetric and point set generation methods particularly toward large solid objects, and it works multiple view angles as well.

Keywords

Point Cloud, Shape analysis, Semantic Segmentation, Convolutional Neural Networks, UNET, Multiple Views

How to Cite this Article?

Dalai, R. (2019). Estimation of volume of a solid object From Three Dimensional Point Clouds Generated By Convolutional Neural Networks based Semantic Segmentation.i-manager’s Journal on Pattern Recognition, 6(1), 27-34. https://doi.org/10.26634/jpr.6.1.16458

References

[1]. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Süsstrunk, S. (2012). SLIC superpixels compared to state-of- the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11), 2274- 2282.
[2]. Bindu., C., H., & Chandra., B. S. (July 2016). Medical images enhancement by homomorphic filtering equalization. International Advanced Research Journal in Science, Engineering and Technology, 3(7), 183-185.
[3]. Cruz, J. P. N., Dimaala, M. L., Francisco, L. G. L., Franco, E. J. S., Bandala, A. A., & Dadios, E. P. (2013, March). Object recognition and detection by shape and color pattern recognition utilizing Artificial Neural Networks. In 2013 International Conference of Information and Communication Technology (ICoICT) (pp. 140-144).
[4]. Dileep, D., & Nair, R. S. (2014, July). Weighted pixel aggregation segmentation on outdoor scene images. In 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT) (pp. 819-823). IEEE. https://doi.org/10.1109/ICCICCT.2014.6993071
[5]. Dunlop, H. (2006). Automatic rock detection and classification in natural scenes, (Masters Thesis, Carnegie Mellon University).
[6]. Feng, D., Haase-Schuetz, C., Rosenbaum, L., Hertlein, H., Duffhauss, F., Glaeser, C., & Dietmayer, K. (2019). Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges. arXiv preprint arXiv:1902.07830.
[7]. Hussin, R., Juhari, M. R., Kang, N. W., Ismail, R. C., & Kamarudin, A. (2012). Digital image processing techniques for object detection from complex background image. Procedia Engineering, 41, 340-344.
[8]. Kalina, J. (2012). Implicitly weighted methods in robust image analysis. Journal of Mathematical Imaging and Vision, 44(3), 449-462.
[9]. Li, K., Pham, T., Zhan, H., & Reid, I. (2018). Efficient dense point cloud object reconstruction using deformation vector fields. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 497-513).
[10]. Lin, C. H., Kong, C., & Lucey, S. (2018, April). Learning efficient point cloud generation for dense 3D object reconstruction. In Thirty-Second AAAI Conference on Artificial Intelligence, 1-10.
[11]. Mineo, C., Pierce, S. G., & Summan, R. (2019). Novel algorithms for 3D surface point cloud boundary detection and edge, Journal of Computational Design and Engineering, 6(1), 81-91. https://doi.org/10.1016/ j.jcde.2018.02.001
[12]. Priya, C. S. (2015). Object weight estimation from 2- images. ARPN Journal of Engineering and Applied Sciences, 10(17), 7574-7578.
[13]. Rethage, D., Wald, J., Sturm, J., Navab, N., & Tombari, F. (2018). Fully-convolutional point networks for large-scale point clouds. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 596-611).
[14]. Sabliov, C. M., Boldor, D., Keener, K. M., & Farkas, B. E. (2002). Image processing method to determine surface area and volume of axi-symmetric agricultural products. International Journal of Food Properties, 5(3), 641-653.
[15]. Terekhin, A. V. (2016, November). Classification model for flat nonconvex images using diagonal segments and tuples for system of automatic recognition of three-dimensional objects. In 2016 Dynamics of Systems, Mechanisms and Machines (Dynamics) (pp. 1- 5). IEEE. https://doi.org/10.1109/Dynamics.2016.7819096
[16]. Wang, W. (2005, July). Image segmentation of irregular shape grains on ceramic material surfaces. In International Conference on Computer Graphics, Imaging and Visualization (CGIV'05) (pp. 49-54). IEEE. DOI: 10.1109/CGIV.2005.44
[17]. Wu, J., Zhang, C., Xue, T., Freeman, B., & Tenenbaum, J. (2016). Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. In Advances in Neural Information Processing Systems (pp. 82-90).
[18]. Xu, X., Li, G., Xie, G., Ren, J., & Xie, X. (2019). Weakly supervised deep semantic segmentation using CNN and ELM with semantic candidate regions. Complexity, 2019. https://doi.org/10.1155/2019/9180391
[19]. Zhang, C., Luo, W., & Urtasun, R. (2018, September). Efficient convolutions for real-time semantic segmentation of 3d point clouds. In 2018 International Conference on 3D Vision (3DV) (pp. 399-408). IEEE.

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