We propose and simulate a method of reconstructing a three-dimensional scene from a two-dimensional image for developing and augmenting world models for autonomous navigation. This is an extension of the Perspective-n-Point (PnP) method which uses a sampling of the 3D scene, 2D image point parings, and Random Sampling Consensus (RANSAC) to infer the pose of the object and produce a 3D mesh of the original scene. Using object recognition and segmentation, we simulate the implementation on a scene of 3D objects with an eye to implementation on embeddable hardware. The final solution will be deployed on the NVIDIA Tegra platform.
Franz Parkins and Eddie Jacobs, "Three-dimensional scene reconstruction from a two-dimensional image," Proc. SPIE 10199, Geospatial Informatics, Fusion, and Motion Video Analytics VII, 1019909 (Presented at SPIE Defense + Security: April 12, 2017; Published: 1 May 2017); https://doi.org/10.1117/12.2266411.
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