Abstract-In this paper we present a new approach for object classification in continuously streamed Lidar point clouds collected from urban areas. The input of our framework is raw 3-D point cloud sequences captured by a Velodyne HDL-64 Lidar, and we aim to extract all vehicles and pedestrians in the neighborhood of the moving sensor. We propose a complete pipeline developed especially for distinguishing outdoor 3-D urban objects. Firstly, we segment the point cloud into regions of ground, short objects (i.e. low foreground) and tall objects (high foreground). Then using our novel two-layer grid structure, we perform efficient connected component analysis on the foreground regions, for producing distinct groups of points which represent different urban objects. Next, we create depth-images from the object candidates, and apply an appearance based preliminary classification by a Convolutional Neural Network (CNN). Finally we refine the classification with contextual features considering the possible expected scene topologies. We tested our algorithm in real Lidar measurements, containing 1159 objects captured from different urban scenarios.

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The predictive performance of five different pK a prediction tools (ACDpKa, Epik, Marvin pKa, Pallas pKa, and VCCpKa) was investigated on the 248-membered Gold Standard dataset. We found VCC as the most predictive, high throughput pK a predictor. However since VCC calculates pK a for the most acidic or basic group only we concluded that ACD and Marvin are in fact the method of choice for medicinal chemistry applications. Analyzing the common outliers we identified guanidines, enolic hydroxyl groups and weak acidic NHs as most problematic moieties from prediction point of view. Our results obtained on the high quality, homogenous Gold Standard dataset could be useful for end-users selecting a suitable solution for pK a prediction.