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3D models of objects and scenes are critical to many academic disciplines and industrial applications. Of particular interest is the emerging opportunity for 3D graphics to serve artificial intelligence: computer vision systems can benefit from synthetically-generated training data rendered from virtual 3D scenes, and robots can be trained to navigate in and interact with real-world environments by first acquiring skills in simulated ones. One of the most promising ways to achieve this is by learning and applying generative models of 3D content: computer programs that can synthesize new 3D shapes and scenes. To allow users to edit and manipulate the synthesized 3D content to achieve their goals, the generative model should also be structure-aware: it should express 3D shapes and scenes using abstractions that allow manipulation of their high-level structure. This state-of-the- art report surveys historical work and recent progress on learning structure-aware generative models of 3D shapes and scenes. We present fundamental representations of 3D shape and scene geometry and structures, describe prominent methodologies including probabilistic models, deep generative models, program synthesis, and neural networks for structured data, and cover many recent methods for structure-aware synthesis of 3D shapes and indoor scenes.
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Functionality representations and applications for shape analysis
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 JD 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 ()f 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 ()f 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 functionality, and conclude the report with a discussion of current challenges and potential future works.
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- Award ID(s):
- 1729205
- PAR ID:
- 10081571
- Date Published:
- Journal Name:
- Eurographics
- Volume:
- 37
- Issue:
- 2
- ISSN:
- 0946-2767
- Page Range / eLocation ID:
- 603-624
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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