Abstract: Methods, apparatuses and systems may provide for conducting a quality assessment of a depth localization mode, a color localization mode and an inertia localization mode, and selecting one of the depth localization mode, the color localization mode or the inertia localization mode as an active localization mode based on the quality assessment. Additionally, a pose of a camera may be determined relative to a three-dimensional (3D) environment in accordance with the active localization mode.

Abstract: Techniques are disclosed for drift correction for camera tracking. In some cases, the techniques include iterative optimization of the camera poses at selected keyframes captured along the camera trajectory to reduce pose errors. Such iterative optimization may include performing an alignment process using the point cloud of a given keyframe and a point cloud made from one or more overlapping keyframes, such as via an iterative closest point (ICP)-based expectation maximization. The keyframes may then be fused to reconstruct a more accurate model of the scene, discarding the existing model. The new model can then be used for tracking and meshing. In some instances, keyframes may be selected such that the overlap with other keyframes includes enough shape features to allow for alignment with the keyframe depth point cloud. In some cases, the techniques can be performed in a particular order giving precedence to keyframes having smaller pose errors.

Abstract: Methods, apparatuses and systems may provide for conducting a quality assessment of a depth localization mode, a color localization mode and an inertia localization mode, and selecting one of the depth localization mode, the color localization mode or the inertia localization mode as an active localization mode based on the quality assessment. Additionally, a pose of a camera may be determined relative to a three-dimensional (3D) environment in accordance with the active localization mode.

Abstract: Techniques are disclosed for drift correction for camera tracking. In some cases, the techniques include iterative optimization of the camera poses at selected keyframes captured along the camera trajectory to reduce pose errors. Such iterative optimization may include performing an alignment process using the point cloud of a given keyframe and a point cloud made from one or more overlapping keyframes, such as via an iterative closest point (ICP)-based expectation maximization. The keyframes may then be fused to reconstruct a more accurate model of the scene, discarding the existing model. The new model can then be used for tracking and meshing. In some instances, keyframes may be selected such that the overlap with other keyframes includes enough shape features to allow for alignment with the keyframe depth point cloud. In some cases, the techniques can be performed in a particular order giving precedence to keyframes having smaller pose errors.