A RangeIndependent DisparityBased Calibration Model for Structured Light PatternBased RGBD Sensor Consumergrade RGBD sensors that provide both colour and depth information have many potential applications, such as robotics control, localization, and mapping, due to their low cost and simple operation. However, the depth measurement provided by consumergrade RGBD sensors is still inadequate for many highprecision applications, such as rich 3D reconstruction, accurate object recognition and precise localization, due to the fact that the systematic errors of RGB sensors increase exponentially with the ranging distance. Most existing calibration models for depth measurement must be carried out with different distances. In this paper, we reveal the mechanism of how an infrared (IR) camera and IR projector contribute to the overall noncentrosymmetric distortion of a structured light patternbased RGBD sensor. Then, a new twostep calibration method for RGBD sensors based on the disparity measurement is proposed, which is rangeindependent and has full frame coverage. Three independent calibration models are used for the calibration for the three main components of the RGBD sensor errors: the infrared camera distortion, the infrared projection distortion, and the infrared conecaused bias. Experiments show the proposed calibration method can provide precise calibration results in fullrange and fullframe coverage of depth measurement. The offset in the edge area of longrange depth (8 m) is reduced from 86 cm to 30 cm, and the relative error is reduced from 11% to 3% of the range distance. Overall, at far range the proposed calibration method can improve the depth accuracy by 70% in the central region of depth frame and 65% in the edge region.

3D Reconstruction of Dense Model based on the Sparse Frames using RGBD Camera With the popularity of consumergrade depth cameras on mobile devices, 3D reconstruction based on the RGBD camera has once again become a hot topic in the field of 3D vision. The current application of RGBD cameras is mainly in the field of largescale 3D reconstruction and VSLAM that rely on highperformance graphics cards to achieve realtime reconstruction and currently reach a relatively mature stage. The reconstruction of specific scene models ( such as objects, human bodies, human faces ) has a lot of application prospects in the fields of intelligent volume measurement, face recognition, motion capture, etc, attracting the attention of researchers. In this paper, a lightweight 3D reconstruction algorithm framework is designed for scene objects. The selecting frame module is introduced. The dense 3D reconstruction based on sparse frames is implemented on the Raspberry Pi 4B to improve the model reconstruction efficiency and we design the point cloud poseprocessing module to improve the quality of the model reconstruction.