BuildAnyPoint: 从多样点云构建三维建筑结构抽象
BuildAnyPoint: 3D Building Structured Abstraction from Diverse Point Clouds
AI总结：
BuildAnyPoint通过Loca-DiT框架从多样点云中生成结构化三维建筑抽象，提升重建的表面准确性和分布均匀性。
摘要：
我们介绍了BuildAnyPoint，一种新颖的生成框架，用于从具有不同分布的点云（如由空中LiDAR和Structure-from-Motion捕捉的点云）中进行结构化三维建筑重建。为了在高度欠约束的环境中恢复艺术家创建的建筑抽象，我们利用显式3D生成先验在自回归网格生成中的作用。具体来说，我们设计了一个松散级联扩散变换器（Loca-DiT），它首先从噪声或稀疏点中恢复底层分布，然后自回归地将它们封装成紧凑的网格。我们首先将分布恢复制定为一个条件生成任务，通过训练潜变量扩散模型，使其在输入点云条件下进行训练，然后基于恢复的点云定制一个解码器-only变压器，用于条件自回归网格生成。我们的方法在定性和定量上都优于先前的建筑抽象方法。此外，我们方法的有效性通过其恢复的点云在建筑点云补全基准上的强大表现得到证明，这些点云在表面准确性和分布均匀性方面有所提高。
英文摘要：
We introduce BuildAnyPoint, a novel generative framework for structured 3D building reconstruction from point clouds with diverse distributions, such as those captured by airborne LiDAR and Structure-from-Motion. To recover artist-created building abstraction in this highly underconstrained setting, we capitalize on the role of explicit 3D generative priors in autoregressive mesh generation. Specifically, we design a Loosely Cascaded Diffusion Transformer (Loca-DiT) that initially recovers the underlying distribution from noisy or sparse points, followed by autoregressively encapsulating them into compact meshes. We first formulate distribution recovery as a conditional generation task by training latent diffusion models conditioned on input point clouds, and then tailor a decoder-only transformer for conditional autoregressive mesh generation based on the recovered point clouds. Our method delivers substantial qualitative and quantitative improvements over prior building abstraction methods. Furthermore, the effectiveness of our approach is evidenced by the strong performance of its recovered point clouds on building point cloud completion benchmarks, which exhibit improved surface accuracy and distribution uniformity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
