This paper presents a simple yet effective method for feature-preserving surface smoothing: HLO. We show that our method can preserve features better than the popular uniform Laplacian-based denoising and it significantly alleviates the shrinkage artifact. Extensive experimental results demonstrate that HLO is better than or comparable to state-of-the-art techniques both qualitatively and quantitatively and that it is particularly good at handling meshes with high noise.

Recently, curvature filter (CF) has been developed to implicitly minimize curvature for image processing problems such as smoothing and denoising. In this paper, we propose a parallel curvature filter (PCF) that performs on GPU which is much faster than the original CF on CPU. Inspired by Convolution Neural Networks processed by GPU, the convolution operations in curvature filter computation can be similarly paralleled by GPU so that the PCF on a single GPU can process 33.2 Giga pixels per second.

In this paper, we investigate a remotely related question: shape transformation between LEGO models. While many computational algorithms have been developed to construct LEGO models, the problem of constructing a LEGO model using bricks from an existing model has not been explored in open literature. We propose two objectives to optimize the transformation: the movement cost and the reuse rate of LEGO bricks.

We present an optimization method for process sequencing in automated assembly of three-dimensional physical structures comprised of uniform elements using robotic equipment. This is part of a process of large-scale construction based on a pick-and-place (PnP) assembly approach. We show that PnP process sequencing is a kind of assignment problem that can be solved by the Hungarian method. The approach adopted in the strategy may be generalized in different application-dependent scenarios, such as from crane operations to large scale 3D printing.