Can I read DAPoinTr: Domain Adaptive Point Transformer for Point Cloud Completion on EtoBox?

DAPoinTr: Domain Adaptive Point Transformer for Point Cloud Completion by Li, Yinghui; Zhou, Qianyu; Gong, Jingyu; Zhu, Ye; Dazeley, Richard; Zhao, Xinkui; Lu, Xuequan is a scholarly article available to read on EtoBox.

What is DAPoinTr: Domain Adaptive Point Transformer for Point Cloud Completion about?

Point Transformers (PoinTr) have shown great potential in point cloud completion recently. Nevertheless, effective domain adaptation that improves transferability toward target domains remains unexplored. In this paper, we delve into this topic and empirically discover that direct feature alignment on point Transformer's CNN backbone only brings limited improvements since it cannot guarantee sequence-wise domain-invariant features in the Transformer. To this end, we propose a pioneering Domain Adaptive Point Transformer (DAPoinTr) framework for point cloud completion. DAPoinTr consists of three key components: Domain Query-based Feature Alignment (DQFA), Point Token-wise Feature alignment (PTFA), and Voted Prediction Consistency (VPC). In particular, DQFA is presented to narrow the global domain gaps from the sequence via the presented domain proxy and domain query at the Transformer encoder and decoder, respectively. PTFA is proposed to close the local domain shifts by aligning the tokens, \emph{i.e.,} point proxy and dynamic query, at the Transformer encoder and decoder, respectively. VPC is designed to consider different Transformer decoders as multiple of experts (MoE) for ense

Author

Li, Yinghui; Zhou, Qianyu; Gong, Jingyu; Zhu, Ye; Dazeley, Richard; Zhao, Xinkui; Lu, Xuequan

Published

2024

Language

EN