Abstract
We present an end-to-end framework for generating artist-style meshes from noisy or incomplete point clouds, such as those captured by real-world sensors like LiDAR or mobile RGB-D cameras. Artist-created meshes are crucial for commercial graphics pipelines due to their compatibility with animation and texturing tools and their efficiency in rendering. However, existing approaches often assume clean, complete inputs or rely on complex multi-stage pipelines, limiting their applicability in real-world scenarios. To address this, we propose an end-to-end method that refines the input point cloud and directly produces high-quality, artist-style meshes. At the core of our approach is a novel reformulation of 3D point cloud refinement as a 2D inpainting task, enabling the use of powerful generative models. Preliminary results on the ShapeNet dataset demonstrate the promise of our framework in producing clean, complete meshes.
Abstract (translated)
我们提出了一种端到端框架，用于从嘈杂或不完整的点云数据中生成符合艺术家风格的网格模型。这些点云数据通常由现实世界中的传感器（如激光雷达或移动RGB-D相机）捕获。艺术家创作的网格模型对于商业图形流水线至关重要，因为它们与动画和纹理工具兼容，并且在渲染效率方面表现出色。然而，现有的方法往往假设输入是干净而完整的，或者依赖于复杂的多阶段管道流程，这限制了它们在现实世界场景中的应用范围。 为了解决这些问题，我们提出了一种端到端的方法，该方法可以优化输入点云数据并直接生成高质量的艺术家风格网格模型。我们的方法核心在于将三维点云细化问题重新表述为二维图像修复任务（即2D inpainting），这样就可以利用强大的生成式模型进行处理。 在ShapeNet数据集上的初步结果显示了我们框架在产生干净且完整网格模型方面的潜力。
URL
https://arxiv.org/abs/2509.12501
