Titlebook: RGB-D Image Analysis and Processing; Paul L. Rosin,Yu-Kun Lai,Yonghuai Liu Book 2019 Springer Nature Switzerland AG 2019 RGB-D Images.Comp

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认为

Commodity RGB-D Sensors: Data Acquisitioniled look into the different types of existing range sensors. We discuss the two fundamental types of commodity range sensing techniques in detail, namely passive and active sensing, and we explore the principles these technologies are based on. Our focus is on modern active commodity range sensors

的染料

Dealing with Missing Depth: Recent Advances in Depth Image Completion and Estimationletion of missing scene depth is contrastingly limited. Furthermore, recent advances in machine learning using deep neural networks have enabled complete depth estimation in a monocular or stereo framework circumnavigating the need for any completion post-processing, hence increasing both efficiency

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Minikin

RGB-D Sensors Data Quality Assessment and Improvement for Advanced Applicationsinformation that can be extracted by the raw data, together with proper criteria for data validation and verification. The present chapter provides an overview of RGB-D sensors technology and an analysis of how random and systematic 3D measurement errors affect the global 3D data quality in the vari

一条卷发

3D Reconstruction from RGB-D Datadistinctive texture. There are two key components to 3D reconstruction from RGB-D data: (1) spatial alignment of the surface over time and, (2) fusion of noisy, partial surface measurements into a more complete, consistent 3D model. In the case of static scenes, the sensor is typically moved around

BRUNT

RGB-D Odometry and SLAM are closely related, as the former aims to track the incremental camera motion with respect to a local map of the scene, and the latter to jointly estimate the camera trajectory and the global map with consistency. In both cases, the standard approaches minimize a cost function using nonlinear opti

娘娘腔

Enhancing 3D Capture with Multiple Depth Camera Systems: A State-of-the-Art Reportse challenges have been addressed and present the most comprehensive review to date of the techniques used to implement multiple depth-sensing camera systems. In addition, we present a wide array of applications supported by multiple depth camera systems (MDCs).

nonradioactive

RGB-D Image-Based Object Detection: From Traditional Methods to Deep Learning Techniquesedented level of performance. We survey the key contributions, summarize the most commonly used pipelines, discuss their benefits and limitations and highlight some important directions for future research.

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