Abstract

3D scene modeling has long been a fundamental problem in computer graphics and computer vision. With the popularity of consumer-level RGB-D cameras, there is a growing interest in digitizing real-world indoor 3D scenes. However, modeling indoor 3D scenes remains a challenging problem because of the complex structure of interior objects and poor quality of RGB-D data acquired by consumer-level sensors. Various methods have been proposed to tackle these challenges. In this survey, we provide an overview of recent advances in indoor scene modeling techniques, as well as public datasets and code libraries which can facilitate experiments and evaluation.

Highlights

  • Consumer-level color and depth (RGB-D) cameras (e.g., Microsoft Kinect) are widely available and are affordable to the general public

  • Most of these datasets were built and labeled for specific applications, such as scene reconstruction, object detection and recognition, scene understanding and segmentation, etc., as long as they provide full RGB-D image streams of indoor scenes, they can be used as input for indoor scene modeling

  • We have presented an extensive survey of indoor scene modeling from RGB-D data

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Summary

Introduction

Consumer-level color and depth (RGB-D) cameras (e.g., Microsoft Kinect) are widely available and are affordable to the general public. Manuscript received: 2015-10-09; accepted: 2015-11-19 people quickly and acquire 3D digital representations of their living surroundings is an urgent yet still challenging research problem. 3D big data has the potential to change the landscape of 3D visual data processing This survey focuses on digitizing real-world indoor scenes, which has received significant interest in recent years. It has many applications which may fundamentally change our daily life. Depth information captured by consumer-level scanning devices is often noisy, may be distorted, and can have large gaps To address these challenges, various methods have been proposed in the past few years and this is still an active research area in both computer graphics and computer vision communities.

Types and properties
Public datasets
Open source libraries
Modeling techniques
Geometric modeling
Semantic modeling
Model-based methods
Conclusions
Full Text
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