# Par3 DNet: Using 3 DCNNs for Object Recognition on Tridimensional Partial Views
## 2 Related Works
In the case of _PointGrid_[20], it incorporates a constant number of points within each grid cell, using a technique they call point quantization, and stacks the points's coordinates as features for each cell, allowing the network to learn better representations of the local geometry of point clouds. However, not only have _CNNs_ been used, but so have novel deep learning architectures: _Vconv-dae_[21] employs a convolutional denoising auto-encoder as a feature learning network, ref. [22] uses a _Variational auto-encoder_ and [23] a _Generative Adversarial Network_. Similar to multiview 2D projections, researches such as _MO-VCNN_[24] explore the advantages of generating different rotations of the 3D models, bu changing azimuth and elevation angles, extracting high level features from every orientation and combining them into a final feature vector. Other works, such as _O-CNN_[25] and _OGN_[26] try to take advantage of the speedup of 3d convolutions obtained by using octree representations of 3D data, along with the possibilities of lower memory consumption compared with fixed size grid representations. The main issue of these techniques is the lack of precision when discretizing an object, which can lose fine details, and the enormous space requirements of the 3D convolutions in memory. **Datasets**. There are some state of the art datasets that have captured single objects in isolation with real sensors for 3D object recognition, as presented in [27]. _RGBD Object Dataset_[28] and _MV-RED_[29] offer captures of 300 and 500 objects using Kinect v1, but without information of the pose. _BigBIRD dataset_[30] and _YCB Object and Model Set_[31] additionally incorporate pose information of 100 objects using an Asus Xtion Pro sensor. However, the biggest effort has been performed by _A large dataset of object scans_[32] that provides captures of more than 10000 objects using a PrimeSense sensor, with the camera pose computed directly from the data. There also are some other widely used datasets such as SUNRGBD [33], SCANNET [34] and S3 DIS [35]. Nonetheless, these datasets are intended for object detection and segmentation, so it is unclear how to adapt them to enable a fair testing protocol for benchmarking object classification approaches. The main problem with the real data is the difficulty to acquire ground truth for 3D pose estimation, that cannot be obtained without external hardware. To solve this issue, many researchers have adopted synthetic datasets. These datasets allow users to control several aspects more carefully, such as point density, pose or noise level. ModelNet [18] provides two different datasets _ModelNet10_ and _ModelNet40_ with thousands of CAD models for 10 and 40 different classes respectively. They are considered de facto standard datasets for 3D object recognition tasks. On the other hand, _ObjectNet_[36], with 100 categories and more than 40 thousand aligned 3D shapes, represents another of the biggest datasets for this purpose. In view of existing methods, we propose an architecture focused on classifying 3D objects by just using one partial view, as the ones provided with sensors like Kinect, in order to improve the results in real applications. Most datasets offer complete views of the 3D objects, so we define a methodology to extract partials views from them, and therefore we take advantage of this large synthetic datasets to classify real 3D scans. Thus, in this paper, we propose Par3 DNet: a deep learning-based architecture for 3D object recognition that takes a partial 3D view of the object as an input and is able to accurately classify it. As there is a lack of large-scale datasets of partial views of real objects, we also focused on filling the gap between synthetic and real data. Despite we trained our system on synthetic data, it is able to generalize and perform accurately on real life objects.