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

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 p...

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Bibliographic Details
Published in:Computer graphics forum Vol. 39; no. 2; pp. 667 - 699
Main Authors: Pintore, Giovanni, Mura, Claudio, Ganovelli, Fabio, Fuentes‐Perez, Lizeth, Pajarola, Renato, Gobbetti, Enrico
Format: Journal Article
Language:English
Published: Oxford Blackwell Publishing Ltd 01.05.2020
Subjects:
Applied computing → Computer‐aided design
CCS Concepts
Computer graphics
Computer vision
Computer vision problems
Computing methodologies → Computer graphics
Image reconstruction
Indoor environments
Reconstruction
Shape inference
Shape modeling
Three dimensional models
ISSN:0167-7055, 1467-8659
Online Access:Get full text
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Summary: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.
Bibliography:Joint first authors
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.14021