ABSTRACT
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. The multi-scale feature extraction module, coarse registration prediction module, and fine registration prediction module designed in this paper can robustly and accurately register two point clouds without iterations. CCRNet can link the feature information between two point clouds and solve the problems of high noise and incomplete overlap by using a soft correspondence matrix. In the standard dataset ModelNet40, in cases of large initial pose difference, high noise, and incomplete overlap, the accuracy of our method, compared with the second-best popular registration algorithm, was improved by 7.0%, 7.8%, and 22.7% on the MAE, respectively. Experiments showed that our CCRNet method has advantages in registration results in a variety of complex conditions.
ABSTRACT
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. Laparoscopic radical gastric gastrectomy and D2 lymph node dissection were performed through the opposite surgical station to the conventional one, followed by digestive tract reconstruction under small incision-assisted direct vision. There was less blood loss throughout the operation, no postoperative complications, and the patient was discharged successfully 10 days after surgery. Histopathological examination showed ulcerated high-medium differentiated adenocarcinoma stage IB (PT2N0M0). There were no complications or tumor recurrence in the patient with examination 6 months after the operation. Conclusion: Surgery in a patient with gastric cancer and SIT can be safely performed by the application of 3D laparoscopy and small incisions to assist the digestive tract reconstruction under direct vision.
ABSTRACT
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. First, according to the low rank of the unwrapped phase map, multiple linear regression analysis is used to obtain the regression plane of the unwrapped phase, and thick PUE positions are marked on the basis of the tolerance set according to the regression plane. Then, an improved median filter is used to mark random PUE positions and finally correct marked PUE. Experimental results show that the proposed method is effective and robust. In addition, this method is progressive in the treatment of highly abrupt or discontinuous regions.
