scholarly journals A Post-Rectification Approach of Depth Images of Kinect v2 for 3D Reconstruction of Indoor Scenes

2017 ◽  
Vol 6 (11) ◽  
pp. 349 ◽  
Author(s):  
Jichao Jiao ◽  
Libin Yuan ◽  
Weihua Tang ◽  
Zhongliang Deng ◽  
Qi Wu
Keyword(s):  
3D Reconstruction ◽  
Kinect V2 ◽  
Depth Images ◽  
Indoor Scenes

scholarly journals A New Model of RGB-D Camera Calibration Based On 3D Control Field

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5082 ◽  
Author(s):  
Zhang ◽  
Huang ◽  
Zhao
Keyword(s):  
3D Reconstruction ◽  
State Of The Art ◽  
Calibration Model ◽  
Control Field ◽  
New Model ◽  
Depth Images ◽  
Extrinsic Parameters ◽  
Relationship Model ◽  
Relative Pose ◽  
Calibration Parameters

With extensive application of RGB-D cameras in robotics, computer vision, and many other fields, accurate calibration becomes more and more critical to the sensors. However, most existing models for calibrating depth and the relative pose between a depth camera and an RGB camera are not universally applicable to many different kinds of RGB-D cameras. In this paper, by using the collinear equation and space resection of photogrammetry, we present a new model to correct the depth and calibrate the relative pose between depth and RGB cameras based on a 3D control field. We establish a rigorous relationship model between the two cameras; then, we optimize the relative parameters of two cameras by least-squares iteration. For depth correction, based on the extrinsic parameters related to object space, the reference depths are calculated by using a collinear equation. Then, we calibrate the depth measurements with consideration of the distortion of pixels in depth images. We apply Kinect-2 to verify the calibration parameters by registering depth and color images. We test the effect of depth correction based on 3D reconstruction. Compared to the registration results from a state-of-the-art calibration model, the registration results obtained with our calibration parameters improve dramatically. Likewise, the performances of 3D reconstruction demonstrate obvious improvements after depth correction.

scholarly journals A Real-Time Online Learning Framework for Joint 3D Reconstruction and Semantic Segmentation of Indoor Scenes

2021 ◽  
pp. 1-1
Author(s):  
Davide Menini ◽  
Suryansh Kumar ◽  
Martin R. Oswald ◽  
Erik Sandstrom ◽  
Cristian Sminchisescu ◽  
...  
Keyword(s):  
Online Learning ◽  
3D Reconstruction ◽  
Real Time ◽  
Semantic Segmentation ◽  
Learning Framework ◽  
Indoor Scenes
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