Functionality Representations and Applications for Shape Analysis

Abstract

A central goal of computer graphics is to provide tools for designing and simulating real or imagined artifacts. An understanding of functionality is important in enabling such modeling tools. Given that the majority of man‐made artifacts are designed to serve a certain function, the functionality of objects is often reflected by their geometry, the way that they are organized in an environment, and their interaction with other objects or agents. Thus, in recent years, a variety of methods in shape analysis have been developed to extract functional information about objects and scenes from these different types of cues. In this report, we discuss recent developments that incorporate functionality aspects into the analysis of 3D shapes and scenes. We provide a summary of the state‐of‐the‐art in this area, including a discussion of key ideas and an organized review of the relevant literature. More specifically, the report is structured around a general definition of functionality from which we derive criteria for classifying the body of prior work. This definition also facilitates a comparative view of methods for functionality analysis. We focus on studying the inference of functionality from a geometric perspective, and pose functionality analysis as a process involving both the geometry and interactions of a functional entity. In addition, we discuss a variety of applications that benefit from an analysis of functionality, and conclude the report with a discussion of current challenges and potential future works.

PlanIT

“…Our graph representation itself is also limited by the small set of relationships it encodes. Support relationships and frequent spatial relationships only scratch the surface of what is possible in terms of analyzing the functionality of 3D objects and scenes [Hu et al 2018]. Fortunately, as our graph generative model architecture is quite general, our relationship graph format is quite extensible.…”

Efficient Calculation of Minimum Distance Between Capsules and Its Use in Robotics

“…The (x, y, z) coordinates of the start and end point of each segment are in the [17], the modified method in [17] and the proposed QR method. (10) where d qr min is the minimum distance calculated using the proposed QR method and d s min is the minimum distance calculated using the method in [17]. Experimental tests resulted in a maximum value of the relative error of 1.059e −8 , a minimum value of the relative error of 1.11e −16 and an average error of 4.87e −10 .…”

“…A collision avoidance framework for mechanisms with complex geometries was demonstrated in simulation environment [9]. Diverse geometries/shapes have been reported in literature for accurate representation of humans and objects, namely in computer graphics science [10]. However, concerning the application in real environment using real sensors, capsules and spheres continue to be the most common geometry applied.…”

FAME: 3D Shape Generation via Functionality-Aware Model Evolution

“…Functionality analysis. Recently, there have been increasing interests in studying 3D shapes from a functional perspective [8]. Existing works on this topic have all focused on characterizing, comparing, or categorizing 3D objects based on their functions, which are typically inferred from their geometry and interactions with other objects or agents [9], [10], [11], [14], [25], [27].…”