This paper proposes a highly parallelized 3D node localization method based on cross-entropy method for the 3D modeling system. Cross-entropy localization statistically estimates node positions from node-to-node distance information by sampling, and each sample evaluation and internal computation of objective function can be processed in parallel. Experimental results show our GPGPU-based implementation achieved 5,163x and 61.5x speed up compared to a single processor and 80-processor implementations. In addition, for enhancing model reproduction accuracy, this work introduces a penalty function to mitigate flip ambiguity.

This paper proposes a 3D node localization method that uses cross-entropy method for the 3D modeling system. The proposed localization method statistically estimates the most probable positions overcoming measurement errors through iterative sample generation and evaluation. The generated samples are evaluated in parallel, and then a significant speedup can be obtained. We also demonstrate that the iterative sample generation and evaluation performed in parallel are highly compatible with interactive node movement.