3D scatter plots are a powerful visualisation method by being able to represent 3 dimensions spatially. It can also enable the representation of additional dimensions, such as by using a colour map. An important issue with the current state of plotting software is the limited use of physical properties from the real world such as shadows to improve the effectiveness of the plots. A popular example is with the use of isometric axes in combination with same-sized points, which is equivalent to removing one whole dimension (depth perception). In static snapshot images, as found in digital and hard prints, as well with discrete data, additional cues such as movement are not present to mitigate for the loss of spatial information. In this paper we present a novel plotting framework that features a wide range of techniques to improve the information transfer from 3D scatterplots for multi-dimensional data. We evaluate the resulting plots by surveying 57 participants from an academic institution to get important insights on what makes 3D scatterplots effective in communicating data of more than two dimensions.

翻译：三维散点图作为一种强大的可视化方法，能够以空间形式呈现三个维度。它还能通过色彩映射等手段表征额外维度。当前绘图软件存在一个重要问题：未能充分利用现实世界中的物理属性（如阴影）来提升图表的表达效果。一个典型的例子是等轴投影与等尺寸点结合使用，这相当于完全移除了一个维度（深度感知）。在静态快照图像（如数字与纸质印刷品）以及离散数据中，缺乏运动等额外线索来弥补空间信息的损失。本文提出了一种新型绘图框架，该框架集成了多种技术以提升多维数据三维散点图的信息传递效率。我们通过调研来自学术机构的57名参与者，对生成的图表进行评估，从而深入探究三维散点图在传达二维以上数据时的有效构成要素。