Data-driven shape analysis and processing
SIGGRAPH ASIA 2016 Courses on - SA '16
Data-driven methods serve an increasingly important role in discovering geometric, structural, and semantic relationships between shapes. In contrast to traditional approaches that process shapes in isolation of each other, data-driven methods aggregate information from 3D model collections to improve the analysis, modeling and editing of shapes. Through reviewing the literature, we provide an overview of the main concepts and components of these methods, as well as discuss their application to
... heir application to classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing. Relation to knowledge-driven shape processing. Prior to the emergence of data-driven techniques, high-level shape understanding and modeling was usually achieved with knowledge-driven methods. In the knowledge-driven paradigm, geometric and structural patterns are extracted and interpreted with the help of explicit rules or hand-crafted parameters. Such examples include heuristicsbased shape segmentation [Shamir 2008] and procedural shape modeling [Müller et al. 2006 ]. Although these approaches have certain empirical success, they exhibit several inherent limitations. First, it is extremely difficult to hard-code explicit rules and heuristics that can handle the enormous geometric and structural variability of 3D shapes and scenes in general. As a result, knowledgedriven approaches are often hard to generalize well to large and diverse shape collections. Another issue is that non-experts find it difficult to interact with knowledge-driven techniques that require as input "low-level" geometric parameters or instructions. In contrast to knowledge driven methods, data-driven techniques learn representations and parameters from data. They usually do not depend on hard-coded prior knowledge, and consequently do not rely on hand-crafted parameters, making these techniques more data-adaptive and thus lead to significantly improved performance in many practical settings. The success of data-driven approaches, backed by machine learning techniques, heavily relies on the accessibility of large data collections. We have witnessed the successful performance improvement of machine learning algorithms by increasing the training set size [Banko and Brill 2001] . In light of this, the recent developments in 3D modeling tools and acquisition techniques for 3D geometry, as well as availability of large repositories of 3D shapes (e.g., Trimble 3D Warehouse, Yobi3D , etc.), offer great opportunities for developing data-driven approaches for 3D shape analysis and processing. Relation to structure-aware shape processing. This report is closely related to the recent survey on "structure-aware shape processing" by Mitra and co-workers ], which concentrates on techniques for structural analysis of 3D shapes, as well as high-level shape processing guided by structure-preservation. In that survey, shape structure is defined as the arrangement and relations between shape parts, which is analyzed through identifying shape parts, part parameters, and part relations. Each of the three can be determined through manual assignment, predefined model fitting and data-driven learning.