# An Efficient Bidirectional Point Pyramid Attention Network for 3D Point Cloud Completion
###### Abstract  
Point cloud completion is a necessary task in real-world applications of recovering a complete geometry from missing regions of 3D objects. Furthermore, model efficiency is of vital importance in computer vision. In this paper, we present an efficient encoder-decoder network that predicts missing point clouds on the basis of incomplete point clouds. There are several advantages to this approach. First, a Mixed Attention Module (MAM) was implemented to obtain the correlational information of points. Second, the proposed Bidirectional Point Pyramid Attention Network (BiPPAN) can achieve simple and fast multiscale feature fusion to capture important features. Lastly, the designed encoder-decoder framework comprises skip connections to capture long-distance dependencies and structural information. We can conclude from the results of the experiments that the proposed network is an efficient and effective method to accomplish point cloud completion tasks.  
3D point cloud; shape completion; mixed attention mechanism; bidirectional point pyramid attention network +
Footnote †: journal: 13 April 2023  
goal of capturing the correlational information of points and selectively convey geometric information from the local regions of incomplete point clouds, so that we could reconstruct complete point clouds. The designed modules enable our model to learn structural features better, and preserve detailed information for point cloud completion to improve prediction accuracy.  
In this work, we demonstrate a method to only generate the missing part of point clouds. Our main contributions are reflected in three aspects and are summed up in Figure 1.  
1. We offer an encoder-decoder network for point cloud completion. A set of experiments proved the effectiveness and robustness of the proposed method on several challenging datasets.
2. We applied a Channelwise Attention Module (CAM) and a Mixed Attention Module (MAM) to introduce a reasonable weight to learn the importance of distinct features. While fusing different input features, the contributions of the fused output features are not equal. The attention mechanism allows for the network to infer the missing regions from incomplete point clouds, exploiting more effective geometric information.
3. We designed a simple and highly effective decoder that consists of skip connections and a Bidirectional Point Pyramid Attention Network (BiPPAN). BiPPAN applies top-down and bottom-up multiscale feature fusion. In fact, a multilayer pyramid network model was used on PF-Net [14]. However, a point cloud generation method propagating low-level features to improve the entire feature hierarchy has not yet been invented.  
To verify whether the proposed network acted effectively, we evaluated our network with extensive experiments on public ShapeNet datasets of three different sizes (ShapeNet-13, ShapeNet-16, ShapeNet-55). We can conclude from the experimental results that our proposed method outperformed PF-Net as the baseline method, whether it was on large or small datasets.  
The remainder of the paper is organized as follows. Section 2 demonstrates the related works. Section 3 presents our network and loss function. Section 4 describes our experiments on the ShapeNet dataset to perform completion in point clouds. Lastly, Section 5 summarizes our paper.