High-fidelity mesh reconstruction from point clouds has long been a fundamental research topic in computer vision and computer graphics. Traditional methods require dense triangle meshes to achieve high fidelity, but excessively dense triangles may lead to unnecessary storage and computational burdens, while also struggling to capture clear, sharp, and continuous edges. This paper argues that the key to high-fidelity reconstruction lies in preserving sharp features. Therefore, we introduce a novel sharp-feature-preserving reconstruction framework based on primitive detection. It includes an improved deep-learning-based primitive detection module and two novel mesh splitting and selection modules that we propose. Our framework can accurately and reasonably segment primitive patches, fit meshes in each patch, and split overlapping meshes at the triangle level to ensure true sharpness while obtaining lightweight mesh models. Quantitative and visual experimental results demonstrate that our framework outperforms both the state-of-the-art learning-based primitive detection methods and traditional reconstruction methods. Moreover, our designed modules are plug-and-play, which not only apply to learning-based primitive detectors but also can be combined with other point cloud processing tasks such as edge extraction or random sample consensus (RANSAC) to achieve high-fidelity results.

The surface mesh reconstruction from point clouds has been a fundamental research topic in Computer Vision and Computer Graphics. Recently, the Neural Implicit Representation (NIR)-based reconstruction has revolutionized this research topic. This work summarizes and analyzes representative works on NIR-based reconstruction and highlights several important insights. However, one major problem with existing works is that they struggle to handle high-resolution meshes. To address this, this paper introduces HRE-NDC, a novel High-Resolution and Efficient Neural Dual Contouring approach for mesh reconstruction from point clouds, which takes the previous state-of-the-art as a baseline and adopts a coarse-to-fine network structure design, along with feature-preserving downsampling and other improvements. HRE-NDC significantly reduces training time and memory usage while achieving better surface reconstruction results. Experimental results demonstrate the superiority of our method in both visualization and quantitative results, and it shows excellent generalization performance on various data including large indoor scenes, real-scanned urban buildings, clothing, and human bodies.