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Abstract

The problem of Surface Reconstruction arises in many real world situations. We introduce in detail the problem itself and then take a brief look into its applications and existing techniques, particularly learning based techniques, developed for its solution. Having presented the context, we closely examine one such learning based technique – the Neural Mesh algorithm for Surface Reconstruction. Despite being relatively recent, the Neural Mesh algorithm has already undergone several revisions, thus giving rise to several variants of the original algorithm. We study the algorithm and each of its variants in detail. All variants rely in varying degrees on a specific aspect of the algorithm – a signal counter. We observe that algorithmic reliance on the signal counter impedes performance and propose an alternate way of performing the same functionalities – using a list. Additionally, on the practical side, we identify areas where inhouse implementations of the algorithms were wanting in efficiency and revise those areas. Changing over from the signal counter to the list represents a change in approach from the exact learning of the original algorithms to a comparative learning framework. We show empirically that this change in approach does not produce any significant difference in the quality of the algorithms’ output, while performance, in terms of running time, increases dramatically.