In this paper we propose surface-reconstructing growing neural gas (SGNG), a learning based artificial neural network that iteratively constructs a triangle mesh from a set of sample points lying on an object?s surface. From these input points SGNG automatically approximates the shape and the topology of the original surface. It furthermore assigns suitable textures to the triangles if images of the surface are available that are registered to the points.
By expressing topological neighborhood via triangles, and by learning visibility from the input data, SGNG constructs a triangle mesh entirely during online learning and does not need any post-processing to close untriangulated holes or to assign suitable textures without occlusion artifacts. Thus, SGNG is well suited for long-running applications that require an iterative pipeline where scanning, reconstruction and visualization are executed in parallel.
Results indicate that SGNG improves upon its predecessors and achieves similar or even better performance in terms of smaller reconstruction errors and better reconstruction quality than existing state-of-the-art reconstruction algorithms. If the input points are updated repeatedly during reconstruction, SGNG performs even faster than existing techniques.
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