Abstract: The light field reconstruction method includes: obtaining a human segmentation result via a pre-trained semantic segmentation network, and obtaining an object segmentation result according to a pre-obtained scene background; fusing multiple frames of depth maps to obtain a geometric model, obtaining a complete human model according to a pre-trained human model completion network, and registering the models by point cloud registration and fusing the registered models to obtain an object model, so as to obtain a complete human model with geometric details and the object model; tracking motion of a rigid object through point cloud registration; reconstructing the complete human model with geometric details through a human skeleton tracking and a non rigid tracking of human surface nodes; and performing a fusion operation in time sequence and obtaining a reconstructed human model and a reconstructed rigid object model through the fusion operation.

Abstract: The disclosure provides 3D reconstruction methods and devices. The method includes: obtaining data captured by the camera and data captured by the inertial measurement unit; obtaining a pose of the camera based on the data; obtaining an adjustment value of the pose of the camera and an adjustment value of bias of the inertial measurement unit; updating the pose of the camera based on the adjustment value of the pose of the camera; determining whether the adjustment value of bias of the inertial measurement unit is less than a preset value; in response to the adjustment value of bias of the inertial measurement unit being greater than or equal to the preset value, determining that a current loop for 3-dimensional reconstruction is an error loop; removing the error loop; and constructing a 3-dimensional model for surroundings of the camera based on the updated pose of the camera and remaining loops.

Abstract: The disclosure provides 3D reconstruction methods and devices. The method includes: obtaining data captured by the camera and data captured by the inertial measurement unit; obtaining a pose of the camera based on the data; obtaining an adjustment value of the pose of the camera and an adjustment value of bias of the inertial measurement unit; updating the pose of the camera based on the adjustment value of the pose of the camera; determining whether the adjustment value of bias of the inertial measurement unit is less than a preset value; in response to the adjustment value of bias of the inertial measurement unit being greater than or equal to the preset value, determining that a current loop for 3-dimensional reconstruction is an error loop; removing the error loop; and constructing a 3-dimensional model for surroundings of the camera based on the updated pose of the camera and remaining loops.