VQ-DcTr: Vector-Quantized Autoencoder With Dual-channel Transformer Points Splitting for 3D Point Cloud Completion

Existing point cloud completion methods mainly utilize the global shape representation to recover the missing regions of the 3D shape from the partial point cloud. However, these methods learn the global shape representations with continuous features against the inherently discrete nature of point cloud, hardly resulting in a high-quality structure for points. To address this challenge, we concentrate on discrete representations, which are potentially a more natural fit for the modalities of the point cloud. Therefore, we propose to employ Vector Quantization (VQ) Auto-Encoder and Dual-channel Transformer for point cloud completion (VQ-DcTr). The VQ-DcTr is apt to use discrete global features and exploit them in a well-structured generation process. Specifically, the vector quantization auto-encoder is integrated to learn a discrete latent representation along with inductive biases inherent in the transformer-based auto-encoder. By using the decoded seeds from the auto-encoder, the dual-channel transformer leverages point-wise and channel-wise attention to learn the splitting patterns in the previous Dual-channel Transformer Points Splitting (DCTPS) layer to perform the points splitting in the current DCTPS layer. In this way, we can obtain the locally compact and structured point cloud by capturing the structure characteristic of 3D shape in local patches. Extensive experiments on all standard benchmarks demonstrate that VQ-DcTr outperforms the state-of-the-art point cloud completion methods through qualitative and quantitative analysis.