State-of-the-art in Automatic 3D Reconstruction of Structured Indoor Environments

Pintore G., Mura C., Ganovelli F., Fuentes-Perez L., Pajarola R., Gobbetti E.

Reconstruction  Computer vision  Computer vision problems  structured reconstruction  CCS Concepts  Computing methodologies  visual computing  Computer-aided design  Computer Graphics and Computer-Aided Design  indoor reconstruction  Applied computing  3D reconstruction  knowledge & systems  Computer graphics  1704 Computer Graphics and Computer-Aided Design  10009 Department of Informatics  Shape modeling  indoor scanning  Shape inference  000 Computer science 

Creating high-level structured 3D models of real-world indoor scenes from captured data is a fundamental task which has important applications in many fields. Given the complexity and variability of interior environments and the need to cope with noisy and partial captured data, many open research problems remain, despite the substantial progress made in the past decade. In this survey, we provide an up-to-date integrative view of the field, bridging complementary views coming from computer graphics and computer vision. After providing a characterization of input sources, we define the structure of output models and the priors exploited to bridge the gap between imperfect sources and desired output. We then identify and discuss the main components of a structured reconstruction pipeline, and review how they are combined in scalable solutions working at the building level. We finally point out relevant research issues and analyze research trends.

Source: Computer graphics forum (Print) 39 (2020): 667–699. doi:10.1111/cgf.14021

Publisher: Basil Blackwell Publishers, Oxford , Paesi Bassi

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