AID-GenReg: Anatomical Identity-Preserving Depth Generation for Enhancing Preoperative-to-Intraoperative Liver Point Cloud Registration

Accurate rigid registration between complete preoperative and partial intraoperative liver point clouds is essential for reliable augmented reality visualization of internal hepatic anatomy during laparoscopic liver resection. However, the scarcity of paired clinical data, coupled with the unavailability of ground-truth transformations, hinders the performance of registration models trained via self-supervision under surgical conditions. Diffusion-based generative data construction presents a promising solution to this data scarcity by synthesizing geometrically diverse observations using limited paired data, producing abundant point cloud pairs to advance downstream registration. Nevertheless, adapting this scheme to clinical registration poses a distinct challenge, as existing methods do not explicitly enforce patient-specific anatomical identity between synthetic intraoperative observations and their corresponding preoperative models, yielding unreliable pairs that deteriorate subsequent self-supervised learning. In this work, we present AID-GenReg, a novel framework that constructs rich anatomy-consistent synthetic pairs to enhance preoperative-to-intraoperative liver point cloud registration. Specifically, AID-GenReg reprojects intraoperative observations and employs a diffusion-based anatomical identity-preserving depth generator to inpaint the resulting missing regions. To preserve anatomical identity represented in the preoperative model, we devise a region-decoupled generation scheme comprising two synergistic mechanisms: Geometry Preservation in observable regions with valid depth and preoperative Anatomy-Guided Completion in complementary missing regions. Such mechanisms ensure that the synthetic pairs offer reliable supervision for registration. Extensive experiments on a public in vivo human liver dataset under three downstream training paradigms demonstrate that AID-GenReg outperforms competitive generative alternatives and achieves consistent gains across multiple registration models.