Fast reconstruction of implicit surfaces using convolutional neural networks 51

Recently, Zhao et al. [Zhao et al. 2024 ] proposed a new method for constructing signed distance functions from fluid simulation particles. This method was able to achieve superior surface smoothness, noise reduction, and temporal coherence compared with previous methods. One of the main limitations of the method was its relatively slow construction times, even though it utilized both the CPU and GPU. In this paper, we consider two modifications to this scheme that make the algorithm easier to optimize without introducing any perceptible changes in reconstruction quality, as illustrated in Figure 1 . With these improvements, a surface can be reconstructed from a single fluid simulation with 2M particles in 2.21 seconds, compared with 72.3 seconds for the original method, resulting in a single-frame reconstruction speedup of about 33 × , making the surface reconstruction fast enough for use within a simulation framework. When reconstructing surface for multiple simulation frames together, we achieve a speedup of about 5 × compared with the original. The optimized implementation will be released with the publication.