# TransPGC: Point Cloud Geometry Compression Based on Transformers
## 1 Introduction  
With the rapid advancement of real-time sensors such as depth cameras and LiDAR, point clouds have found widespread applications in Simultaneous Location And Mapping (SLAM) [1], 3D reconstruction [2], object detection [3], and more. Spatial data collection has been considerably improved with the invention of LiDAR and other laser scanning technologies [4]. However, the increased volume of point clouds leads to higher transmission costs, making it imperative to research point cloud compression algorithms. Point cloud geometry compression methods can be broadly classified into two categories: lossy geometry compression and lossless geometry compression. Lossy geometric compression algorithms have been extensively studied due to their high compression ratio properties [5, 6, 7, 8]. Lossless geometry compression aims to strike a balance between compressed data size and data quality [9, 10, 11, 12, 13]. When more bits are used, it is possible to obtain a value closer to the input. This concept is formalized in the basic rate-distortion tradeoff, where \"rate\" stands for bit rate and \"distortion\" is formalized as the difference between the original and disordered point clouds.  
The sparsity and disorder of point clouds pose significant challenges to data processing and compression. Consequently, point clouds are converted into structured representations, such as tree structures, heightmaps, or voxel representations, which are then compressed using their inherent spatial correlation. With the emergence of deep learning-based methods,several studies have explored neural network-based point cloud compression. Previous research [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15] focused on voxel-based approaches, while [16; 17; 18] utilized tree structures, and [19] employed a heightmap representation. Deep learning-based methods achieve superior compression performance compared to traditional algorithms by learning more memory-efficient encoding strategies from training data. However, there is still space for improvement in these methods.  
This paper presents a point cloud geometry compression based on the Transformer method (TransPCGC). The main contributions of this scheme are as follows:  
* We design a novel Transformer-based network for compressing geometric information in point clouds. This network comprises preprocessing modules for voxelization and partitioning of point clouds, compression networks to optimize the BD-Rate, and post-processing modules for point cloud reconstruction. In contrast to pure convolutional neural networks, our designed network achieves remarkable compression gains.
* The effectiveness of point cloud compression is hindered due to convolutions designed with fixed receptive fields and fixed weights, which cannot aggregate enough information from sparse and disordered point clouds. There is a need for a solution that can better exploit correlations among global and local information point clouds. As the Transformer architecture designed for processing images is not well suited for point clouds, we develop a Transformer capable of handling point clouds. Utilizing this Transformer, we extract the richness of both global and local features from the point cloud.
* Extensive experiments validate the compression performance of the proposed TransPCGC on multiple point cloud datasets, while also demonstrating its low spatial and temporal complexity.  
The remainder of this paper is as follows: Section 2 provides a summary overview of the related work; Section 3 describes our proposed compression and decompression network; Section 4 offers the experimental details and presents the experimental results; Section 5 briefly concludes the paper.