# Par3 DNet: Using 3 DCNNs for Object Recognition on Tridimensional Partial Views
## 5 Conclusions and Future Work  
In this work we introduce Par3 DNet, a 3 DCNN for object recognition that take as an input pure tridimensional data. Our strongest contribution is that our architecture is able to classify partial views of the object, as if the input data were captured by a real sensor. Actually, preliminary tests indicate that our model performs accurately on point clouds provided by a Kinect camera. We focused on the ModelNet10 as this is the most used state of the art dataset for 3D object recognition. We also successfully tested our approach with ObjectNet and ShapeNetCore datasets.  
Despite our model performing accurately overall, it has some limitations. For instance, the bathtub category does not perform as good as the rest of the categories. However, this is understandable as this category has the lowest number of samples by a large margin. There also is a consistent confusion between table and desk. As discussed before, these two categories represent barely the same object. We considered them as two different categories to follow the ModelNet10 rules and to allow an easy comparison with other algorithms. The source code of the approach and the model we used for all the experiments can be downloaded from our public repository [[https://bitbucket.org/fgd5/par3](https://bitbucket.org/fgd5/par3) dnet]([https://bitbucket.org/fgd5/par3](https://bitbucket.org/fgd5/par3) dnet).  
Regarding the computation time, a feedforward step of the architecture only takes 9 ms in the hardware described before. Nonetheless, the generation of the binary voxel representation takes 140 ms. Finally, the full pipeline takes 170 ms from end to end.  
As for the future work, we plan to involve noisy data at training time to further improve the accuracy and robustness of the system. We also plan to include more categories and samples by merging different datasets. In addition, we are also after gathering, and potentially recording, a proper dataset of segmented 3D objects provided by real sensors. We will also append a pose estimation system to the network, so that the architecture is able to provide not only the classification of the input point cloud, but the position and orientation of the objects in the camera's coordinate frame too. Finally, we also plan to develop a method to automatically segment the object from the background, as this is being done manually so far.  
Conceptualization, methodology, writing--review and editing, supervision, project administration and funding acquisition, M.C.; Software, validation, formal analysis, resources, visualization, writing--original draft preparation, F.E.; Conceptualization, writing--original draft preparation, software, validation, formal analysis, data curation, visualization, F.G.-D. All authors have read and agreed to the published version of the manuscript.  
This work has been funded by the Spanish Government TIN2016-76515-R grant for the COMBAHO project, supported with Feder funds. It has also been supported by Spanish grants for PhD studies ACIF/2017/243 and FPU16/00887.  
The authors would like to thank NVIDIA (Santa Clara, California, USA) for the generous donation of a Titan Xp and a Quadro P6000.  
The authors declare no conflict of interest.