Real-time tracking of surrounding objects in augmented and mixed reality applications

Today one of the most important problems in modern Augmented and Mixed reality applications is the analysis of surrounding objects in order to trace variability of the observed environment, the behavior of which is usually unpredictable. The solution to this problem can be widely applied in many mobile applications as a tool to interact with surrounding objects in the environment. Augmented and Mixed realities are both extremely promising directions for further research of interaction with the real environment. In this paper, we suggest a method of creation of geometric data based on a series of points in order to reconstruct the surface of real objects. This allows us to ensure the interaction with virtual objects. The proposed method comprises three steps: point detection, points clusterization into multiple groups of points, depending on their location, and the creation of geometric data such as lines and surfaces.

[1]  Silvio Savarese,et al.  Joint 2D-3D-Semantic Data for Indoor Scene Understanding , 2017, ArXiv.

[2]  Dengxin Dai,et al.  Don’t Forget The Past: Recurrent Depth Estimation from Monocular Video , 2020, IEEE Robotics and Automation Letters.

[3]  P. Milgram,et al.  A Taxonomy of Mixed Reality Visual Displays , 1994 .

[4]  Gabriela Kiryakova The Immersive Power of Augmented Reality , 2020 .

[5]  Ronald Azuma,et al.  A Survey of Augmented Reality , 1997, Presence: Teleoperators & Virtual Environments.

[6]  Cao Sanxing,et al.  A Review of VSLAM Technology Applied in Augmented Reality , 2020 .

[7]  Matthias Nießner,et al.  ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Branislav Sobota,et al.  Mixed Reality and Three-Dimensional Computer Graphics , 2020 .

[9]  Richard Szeliski,et al.  Consistent video depth estimation , 2020, ACM Trans. Graph..

[10]  Leonidas J. Guibas,et al.  PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  David Kim,et al.  DepthLab: Real-time 3D Interaction with Depth Maps for Mobile Augmented Reality , 2020, UIST.