Yabin Zhang

All Publications

• Point-DAE: Denoising Autoencoders for Self-Supervised Point Cloud Learning IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS Zhang, Y., Lin, J., Li, R., Jia, K., Zhang, L. 2025

  Abstract

  Masked autoencoder (MAE) has demonstrated its effectiveness in self-supervised point cloud learning. Considering that masking is a kind of corruption, in this work we explore a more general denoising autoencoder for point cloud learning (Point-DAE) by investigating more types of corruptions beyond masking. Specifically, we degrade the point cloud with certain corruptions as input, and learn an encoder-decoder model to reconstruct the original point cloud from its corrupted version. Three corruption families (i.e., density/masking, noise, and affine transformation) and a total of 14 corruption types are investigated with traditional non-Transformer encoders. Besides the popular masking corruption, we identify another effective corruption family, i.e., affine transformation. The affine transformation disturbs all points globally, which is complementary to the masking corruption where some local regions are dropped. We also validate the effectiveness of affine transformation corruption with the Transformer backbones, where we decompose the reconstruction of the complete point cloud into the reconstructions of detailed local patches and rough global shape, alleviating the position leakage problem in the reconstruction. Extensive experiments on tasks of object classification, few-shot learning, robustness testing, part segmentation, and 3-D object detection validate the effectiveness of the proposed method. The codes are available at https://github.com/YBZh/Point-DAE.

  View details for DOI 10.1109/TNNLS.2025.3557055

  View details for Web of Science ID 001480295300001

  View details for PubMedID 40279235

• ScaleDreamer: Scalable Text-to-3D Synthesis with Asynchronous Score Distillation Ma, Z., Wei, Y., Zhang, Y., Zhu, X., Lei, Z., Zhang, L. edited by Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. SPRINGER INTERNATIONAL PUBLISHING AG. 2025: 1-19

  View details for DOI 10.1007/978-3-031-72667-5_1

  View details for Web of Science ID 001346380800001