Minghui Sun

Guangzhe Hou, Guihe Qin, Minghui Sun et al.

Point clouds obtained from capture devices or 3D reconstruction techniques are often noisy and interfere with downstream tasks. The paper aims to recover the underlying surface of noisy point clouds. We design a novel model, NoiseTrans, which uses transformer encoder architecture for point cloud denoising. Specifically, we obtain structural similarity of point-based point clouds with the assistance of the transformer's core self-attention mechanism. By expressing the noisy point cloud as a set of unordered vectors, we convert point clouds into point embeddings and employ Transformer to generate clean point clouds. To make the Transformer preserve details when sensing the point cloud, we design the Local Point Attention to prevent the point cloud from being over-smooth. In addition, we also propose sparse encoding, which enables the Transformer to better perceive the structural relationships of the point cloud and improve the denoising performance. Experiments show that our model outperforms state-of-the-art methods in various datasets and noise environments.

Minghui Sun, Zhaoyang Chen, Georgios Bakirtzis et al.

Designing systems is typically uncertain and ambiguous at early stages. Set-based design supports alternative exploration and gradual uncertainty reduction during the early lifecycle, making it practical for complex systems design. In parallel, the functional requirements decomposition helps to advance the design incrementally. However, current literature on set-based design lacks formal guidance in how to decompose functional requirements. To bridge this gap, we introduce a four-step method to decompose functional requirements for set-based design hierarchically. We systematically define, reason, and narrow the sets, breaking down the functional requirements into formal sub-requirements. This method allows parallel abstraction, ensuring the resulting system satisfies the top-level functional requirements.