| Citation: | LIU G X，ZHANG J T，DING D D. Lossy point cloud geometry compression based on Transformer[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（2）：634-642 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0412
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Point clouds are widely used for 3D object representation, however, real-world captured point clouds often have huge data, which is unfavorable for transmission and storage. To address the redundancy problem of point cloud data, an end-to-end Transformer-based multiscale point cloud geometry compression method is proposed by introducing the Transformer module based on the attention mechanism. The point cloud is voxelized, features are extracted using sparse convolution at the encoder, multi-scale gradual downsampling is performed, and the Transformer module is combined to enhance the point-space feature perception and extraction; at the decoder, the corresponding multi-scale up-sampling is performed for reconstruction, and the Transformer module is also used to enhance and recover the useful features, and the point cloud is progressively refined and reconstructed. Compared with two standard point cloud coding methods, the proposed method obtains 80% and 75% BD-Rate gain on average; compared with the deep learning-based point cloud compression method, it obtains 16% BD-Rate gain on average, and there is about 0.6 PSNR enhancement at the same bit rate. The experimental results demonstrate the feasibility and effectiveness of Transformer in the field of point cloud compression. In terms of subjective quality, the proposed method also has significant subjective effect improvement, and the reconstructed point cloud is closer to the original point cloud.
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