Robust Point Cloud Registration Network for Complex Conditions.

Point cloud registration is widely used in autonomous driving, SLAM, and 3D reconstruction, and it aims to align point clouds from different viewpoints or poses under the same coordinate system. However, point cloud registration is challenging in complex situations, such as a large initial pose difference, high noise, or incomplete overlap, which will cause point cloud registration failure or mismatching. To address the shortcomings of the existing registration algorithms, this paper designed a new coarse-to-fine registration two-stage point cloud registration network, CCRNet, which utilizes an end-to-end form to perform the registration task for point clouds.

Case report: Laparoscopic-assisted distal gastrectomy for gastric cancer in a patient with situs inversus totalis.

Background: Situs inversus totalis (SIT) is a rare congenital disease with a series of clinical features characterized by a mirror image distribution of the viscera to the normal anatomy.

Case Presentation: This study aims to report a 63-year-old male SIT patient with gastric cancer with a preoperative diagnosis of stage IIB gastric cancer (cT3N0M0), who underwent a preoperative multi-disciplinary treatment (MDT) discussion and an abdominal enhancement CT for visceral evaluation to ensure a successful operation. A laparoscopic-assisted distal gastrectomy including D2 lymph node dissection and Billroth I reconstruction was successfully performed.

Phase Unwrapping Error Correction Based on Multiple Linear Regression Analysis.

Fringe projection profilometry (FPP) is prone to phase unwrapping error (PUE) due to phase noise and measurement conditions. Most of the existing PUE-correction methods detect and correct PUE on a pixel-by-pixel or partitioned block basis and do not make full use of the correlation of all information in the unwrapped phase map. In this study, a new method for detecting and correcting PUE is proposed.