A Survey on Non-photorealistic Rendering Approaches for Point Cloud Visualization.

Point clouds are widely used as a versatile representation of 3D entities and scenes for all scale domains and in a variety of application areas, serving as a fundamental data category to directly convey spatial features. However, due to point sparsity, lack of structure, irregular distribution, and acquisition-related inaccuracies, results of point cloud visualization are often subject to visual complexity and ambiguity. In this regard, non-photorealistic rendering can improve visual communication by reducing the cognitive effort required to understand an image or scene and by directing attention to important features. In the last 20 years, this has been demonstrated by various non-photorealistic rendering approaches that were proposed to target point clouds specifically. However, they do not use a common language or structure for assessment which complicates comparison and selection. Further, recent developments regarding point cloud characteristics and processing, such as massive data size or web-based rendering are rarely considered. To address these issues, we present a survey on non-photorealistic rendering approaches for point cloud visualization, providing an overview of the current state of research. We derive a structure for the assessment of approaches, proposing seven primary dimensions for the categorization regarding intended goals, data requirements, used techniques, and mode of operation. We then systematically assess corresponding approaches and utilize this classification to identify trends and research gaps, motivating future research in the development of effective non-photorealistic point cloud rendering methods.

Large-Scale Evaluation of Topic Models and Dimensionality Reduction Methods for 2D Text Spatialization.

Topic models are a class of unsupervised learning algorithms for detecting the semantic structure within a text corpus. Together with a subsequent dimensionality reduction algorithm, topic models can be used for deriving spatializations for text corpora as two-dimensional scatter plots, reflecting semantic similarity between the documents and supporting corpus analysis. Although the choice of the topic model, the dimensionality reduction, and their underlying hyperparameters significantly impact the resulting layout, it is unknown which particular combinations result in high-quality layouts with respect to accuracy and perception metrics. To investigate the effectiveness of topic models and dimensionality reduction methods for the spatialization of corpora as two-dimensional scatter plots (or basis for landscape-type visualizations), we present a large-scale, benchmark-based computational evaluation. Our evaluation consists of (1) a set of corpora, (2) a set of layout algorithms that are combinations of topic models and dimensionality reductions, and (3) quality metrics for quantifying the resulting layout. The corpora are given as document-term matrices, and each document is assigned to a thematic class. The chosen metrics quantify the preservation of local and global properties and the perceptual effectiveness of the two-dimensional scatter plots. By evaluating the benchmark on a computing cluster, we derived a multivariate dataset with over 45 000 individual layouts and corresponding quality metrics. Based on the results, we propose guidelines for the effective design of text spatializations that are based on topic models and dimensionality reductions. As a main result, we show that interpretable topic models are beneficial for capturing the structure of text corpora. We furthermore recommend the use of t-SNE as a subsequent dimensionality reduction.

Pesticide exposure assessment for surface waters in the EU. Part 2: Determination of statistically based run-off and drainage scenarios for Germany.

BACKGROUND: In order to assess surface water exposure to active substances of plant protection products (PPPs) in the European Union (EU), the FOCUS (FOrum for the Co-ordination of pesticide fate models and their USe) surface water workgroup introduced four run-off and six drainage scenarios for Step 3 of the tiered FOCUSsw approach. These scenarios may not necessarily represent realistic worst-case situations for the different Member States of the EU. Hence, the suitability of the scenarios for risk assessment in the national authorisation procedures is not known. RESULTS: Using Germany as an example, the paper illustrates how national soil-climate scenarios can be developed to model entries of active substances into surface waters from run-off and erosion (using the model PRZM) and from drainage (using the model MACRO). In the authorisation procedure for PPPs on Member State level, such soil-climate scenarios can be used to determine exposure endpoints with a defined overall percentile. CONCLUSION: The approach allows the development of national specific soil-climate scenarios and to calculate percentile-based exposure endpoints. The scenarios have been integrated into a software tool analogous to FOCUS-SWASH which can be used in the future to assess surface water exposure in authorisation procedures of PPPs in Germany. © 2017 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Pesticide exposure assessment for surface waters in the EU. Part 1: Some comments on the current procedure.

In 2001, the European Commission introduced a risk assessment project known as FOCUS (FOrum for the Coordination of pesticide fate models and their USe) for the surface water risk assessment of active substances in the European Union. Even for the national authorisation of plant protection products (PPPs), the vast majority of EU member states still refer to the four runoff and six drainage scenarios selected by the FOCUS Surface Water Workgroup. However, our study, as well as the European Food Safety Authority (EFSA), has stated the need for various improvements. Current developments in pesticide exposure assessment mainly relate to two processes. Firstly, predicted environmental concentrations (PECs) of pesticides are calculated by introducing model input variables such as weather conditions, soil properties and substance fate parameters that have a probabilistic nature. Secondly, spatially distributed PECs for soil-climate scenarios are derived on the basis of an analysis of geodata. Such approaches facilitate the calculation of a spatiotemporal cumulative distribution function (CDF) of PECs for a given area of interest and are subsequently used to determine an exposure concentration endpoint as a given percentile of the CDF. For national PPP authorisation, we propose that, in the future, exposure endpoints should be determined from the overall known statistical PEC population for an area of interest, and derived for soil and climate conditions specific to the particular member state. © 2016 Society of Chemical Industry.