M$^3$CS: Multi-Target Masked Point Modeling with Learnable Codebook and Siamese Decoders
This work addresses the need for versatile point cloud pre-training models to improve downstream tasks like classification and segmentation, representing an incremental advancement in self-supervised learning for 3D data.
The paper tackles the problem of self-supervised pre-training for point clouds by proposing M$^3$CS, which uses multi-target masked point modeling with learnable codebook and siamese decoders to capture both low- and high-level representations, achieving superior performance in classification and segmentation tasks compared to existing methods.
Masked point modeling has become a promising scheme of self-supervised pre-training for point clouds. Existing methods reconstruct either the original points or related features as the objective of pre-training. However, considering the diversity of downstream tasks, it is necessary for the model to have both low- and high-level representation modeling capabilities to capture geometric details and semantic contexts during pre-training. To this end, M$^3$CS is proposed to enable the model with the above abilities. Specifically, with masked point cloud as input, M$^3$CS introduces two decoders to predict masked representations and the original points simultaneously. While an extra decoder doubles parameters for the decoding process and may lead to overfitting, we propose siamese decoders to keep the amount of learnable parameters unchanged. Further, we propose an online codebook projecting continuous tokens into discrete ones before reconstructing masked points. In such way, we can enforce the decoder to take effect through the combinations of tokens rather than remembering each token. Comprehensive experiments show that M$^3$CS achieves superior performance at both classification and segmentation tasks, outperforming existing methods.