Miniature 3D Depth Camera for Realtime Reconstruction This paper developed a miniature 3D vision system for dynamic depth recovery. Our system is constituted by a DOE projector and two cameras. A single pattern with pseudorandom speckles was projected onto the surface of the target object and the coupling pattern was imaged by the stereo cameras. An optimal camera calibration algorithm is adopted to guarantee the measurement accuracy. The speckle pattern projected through a diffractive optical element (DOE) of size 8mmxc3x978mm. The baseline between the stereo cameras is as short as 50mm. The system has the advantages of simple structure, small size, and low cost. It can be easily integrated into mobile devices such as tablet computers and mobile phones. In comparison with several other mature products, the proposed 3D depth camera can achieve 3D reconstruction with higher accuracy.

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.