Plane segmentation is a basic task in the automatic reconstruction of indoor and urban environments from unorganized point clouds acquired by laser scanners. As one of the most common plane-segmentation methods, standard Random Sample Consensus (RANSAC) is often used to continually detect planes one after another. However, it suffers from the spurious-plane problem when noise and outliers exist due to the uncertainty of randomly sampling the minimum subset with 3 points. An improved RANSAC method based on Normal Distribution Transformation (NDT) cells is proposed in this study to avoid spurious planes for 3D point-cloud plane segmentation. A planar NDT cell is selected as a minimal sample in each iteration to ensure the correctness of sampling on the same plane surface. The 3D NDT represents the point cloud with a set of NDT cells and models the observed points with a normal distribution within each cell. The geometric appearances of NDT cells are used to classify the NDT cells into planar and non-planar cells. The proposed method is verified on three indoor scenes. The experimental results show that the correctness exceeds 88.5% and the completeness exceeds 85.0%, which indicates that the proposed method identifies more reliable and accurate planes than standard RANSAC. It also executes faster. These results validate the suitability of the method.

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Fibrinogen-like protein 2 (FGL2) is highly expressed in various tumour tissues and plays a vital role in tumour initiation and progression. This study evaluated the clinical significance of FGL2 in patients with clear cell renal cell carcinoma (ccRCC). FGL2 expression in fresh and 170 archived paraffin-embedded ccRCC tissues was measured by quantitative RT-PCR, western blotting, and immunohistochemitry. FGL2 expression was significantly upregulated in ccRCC. Statistical analyses by using Kaplan–Meier method showed that high FGL2 expression was associated with poor overall survival (OS) and recurrence-free survival (RFS) of patients with ccRCC. Multivariate analyses indicated that FGL2 was as an independent prognostic factor of survivaland that tumoural FGL2 levels could significantly predict the prognosis of patients with early-stage ccRCC. Nomogram systems, which integrated FGL2 expression and other clinical parameters, were established and were found to be better than TNM staging in predicting the OS and RFS of patients with ccRCC. FGL2 silencing led to a significant reduction in cells viability and increase in cells apoptosis, accompanied with a reduced ERK1/2 and p38 MAPK activation, in ccRCC cells. Thus, our results suggest that high FGL2 expression is a novel, independent, and an adverse prognostic factor of clinical outcomes in patients with ccRCC.

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