This paper presents an approach and performance evaluation of performing service-based image processing using software rendering implemented using Mesa3D. Due to recent advances in cloud computing technology (w.r.t. both, hardware and software) as well as increased demands of image processing and analysis techniques, often within an eco-system of devices, it is feasible to research and quantify the impact of service-based approaches in this domain w.r.t. cost-performance relation. For it, we provide a performance comparison for service-based processing using GPU-accelerated and software rendering.
Signed distance functions computed in discrete form from given RGB-D data as regular voxel grids can represent manifold shapes as the zero crossing of a trivariate function; the corresponding meshes can be derived by the Marching Cubes algorithm. However, 3D models automatically reconstructed in this way often contain irrelevant objects or artifacts, such as holes or noise, due to erroneous scan data and error-prone reconstruction processes. This paper presents an approach for interactive editing of signed distance functions, derived from RGB-D data in the form of regular voxel grids, that enables the manual refinement and enhancement of reconstructed 3D geometry. To this end, we combine concepts known from constructive solid geometry, where complex models are created from simple base shapes, with the voxel-based representation of geometry reconstructed from real-world scans. Our approach can be implemented entirely on GPU to enable real-time interaction. Further, we present how to implement high-level operators, such as copy, move, and unification.
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