SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation

We introduce Similarity Group Proposal Network (SGPN), a simple and intuitive deep learning framework for 3D object instance segmentation on point clouds. SGPN uses a single network to predict point grouping proposals and a corresponding semantic class for each proposal, from which we can directly extract instance segmentation results. Important to the effectiveness of SGPN is its novel representation of 3D instance segmentation results in the form of a similarity matrix that indicates the similarity between each pair of points in embedded feature space, thus producing an accurate grouping proposal for each point. To the best of our knowledge, SGPN is the first framework to learn 3D instance-aware semantic segmentation on point clouds. Experimental results on various 3D scenes show the effectiveness of our method on 3D instance segmentation, and we also evaluate the capability of SGPN to improve 3D object detection and semantic segmentation results. We also demonstrate its flexibility by seamlessly incorporating 2D CNN features into the framework to boost performance

SVM-based synthetic fingerprint discrimination algorithm and quantitative optimization strategy.

Synthetic fingerprints are a potential threat to automatic fingerprint identification systems (AFISs). In this paper, we propose an algorithm to discriminate synthetic fingerprints from real ones. First, four typical characteristic factors-the ridge distance features, global gray features, frequency feature and Harris Corner feature-are extracted. Then, a support vector machine (SVM) is used to distinguish synthetic fingerprints from real fingerprints. The experiments demonstrate that this method can achieve a recognition accuracy rate of over 98% for two discrete synthetic fingerprint databases as well as a mixed database. Furthermore, a performance factor that can evaluate the SVM's accuracy and efficiency is presented, and a quantitative optimization strategy is established for the first time. After the optimization of our synthetic fingerprint discrimination task, the polynomial kernel with a training sample proportion of 5% is the optimized value when the minimum accuracy requirement is 95%. The radial basis function (RBF) kernel with a training sample proportion of 15% is a more suitable choice when the minimum accuracy requirement is 98%

Analgesic Effect of Ultrasound-Guided Anterior Quadratus Lumborum Block at the L2 Level in Patients Undergoing Laparoscopic Partial Nephrectomy: A Single-Center, Randomized Controlled Trial

Objectives. This study aimed to evaluate the effect of ultrasound-guided anterior quadratus lumborum block (QLB) at the L2 level in patients undergoing laparoscopic partial nephrectomy. Methods. Patients who were 18–70 years old with an American Society of Anesthesiologists (ASA) physical status of 1-2 and were scheduled for elective laparoscopic partial nephrectomy were recruited into the cluster randomized controlled trial. Sixty-three patients were randomly allocated to receive QLB (group Q, n = 32) or no block (group C, n = 31). The patients were not masked to the group allocations. The postoperative follower was blinded to the group allocations. All patients received total intravenous anesthesia, the same multimodal analgesic regimen, and rescue analgesia when needed. The primary outcome was perioperative cumulative sufentanil consumption. Results. 30 patients in group Q and 29 patients in group C were included in the statistical analysis. Block-related complications were not found in this study. Sufentanil consumption during the perioperative period (155.41 [19.58] vs 119.37 [12.41] μg, p < 0.001) and sufentanil dosage during surgery and 0–6 h, 6–12 h, and 12–24 h after surgery were lower in group Q than in group C, while 24–48 h after surgery was similar between both groups. The median sensory blockade area in group Q was T9-L1. Comparison of invasive blood pressure (BP) and heart rate (HR) before and after skin incision in group C was statistically significant, but there was no significant difference in group Q. Both at rest and during activity, numerical rating scale (NRS) scores and the incidence of rescue analgesia were lower in group Q at any time point after surgery. The incidences of postoperative nausea and vomiting (PONV), time from postoperative to discharge, postoperative recovery quality, or anesthesia satisfaction were similar between the two groups. Conclusions. Anterior QLB at the L2 level can reduce the perioperative dosage of sufentanil and the degree of postoperative pain in patients undergoing laparoscopic partial nephrectomy, but it did not improve postoperative recovery quality and anesthesia satisfaction

The comparison between different kernel functions.

<p>The comparison between different kernel functions.</p

The accuracy rate of different synthetic databases on different kernel functions.

<p>The accuracy rate of different synthetic databases on different kernel functions.</p

Fingerprint's frequency analysis.

<p>(A) A real fingerprint, (B) a DB4 fingerprint, (C) a FPGenerator fingerprint, (D) (A)'s DFT analysis, (E) (B)'s DFT analysis, (F) (C)'s DFT analysis.</p

Typical data distribution of global gray features.

<p>(A) Distribution of <i>Q_grayavg</i>, (b) distribution of <i>Q_grayvar</i>.</p