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.

Accurate 3D building reconstruction is crucial for advancing urban digital twinning, city planning, and sustainable development. As a key architectural component, rooftops facilitate urban energy management and inform urban morphological analysis. Consequently, achieving precise and scalable rooftop reconstruction has emerged as a key research focus in recent years. Point clouds, with their ability to preserve detailed geometric structures, are well suited for this task. However, existing methods predominantly target synthetic rooftop datasets, which lack architectural diversity and often require high-quality point clouds as input. These limitations hinder their applicability to large-scale, real-world urban environments characterized by varied rooftop designs and noisy or sparse data. To address these challenges, we propose a novel end-to-end framework for rooftop wireframe reconstruction from airborne laser scanning (ALS) point clouds. Our approach introduces a multi-scale local feature descriptor optimized for rooftops to enhance per-point geometric feature extraction. Then, a hypergraph-based attention fusion module integrates these features. After comprehensive feature learning by a robust backbone, initial corner detection is followed by a Transformer- and EdgeConv-enhanced edge classification mechanism that models topological relationships through long-range dependencies. Experiments on the large-scale real-world Building3D dataset demonstrate significant improvements over the baseline, with corner accuracy improved by 35% on the Entry-level subset and 41% on the Tallinn subset. Qualitative comparisons further reveal superior wireframe fidelity, underscoring the method’s potential to support digital twinning, urban management, and economic development in smart city initiatives.

Semantic segmentation of large-scale outdoor point clouds is of significant importance in environment perception and scene understanding. However, this task continues to present a significant research challenge, due to the inherent complexity of outdoor objects and their diverse distributions in real-world environments. In this study, we propose the multilateral cascading network (MCNet) designed to address this challenge. The model comprises two key components: a multilateral cascading attention enhancement (MCAE) module, which facilitates the learning of complex local features through multilateral cascading operations; and a point cross-stage partial (P-CSP) module, which fuses global and local features, thereby optimizing the integration of valuable feature information across multiple scales. Our proposed method demonstrates superior performance relative to state-of-the-art approaches across two widely recognized benchmark datasets: Toronto3D and SensatUrban. Especially on the city-scale SensatUrban dataset, our results surpassed the current best result by 2.1% in overall mean intersection over union (mIoU) and yielded an improvement of 15.9% on average for small-sample object categories comprising less than 2% of the total samples, in comparison to the baseline method. Our code is available at https://github.com/ranhaogong/MCNet.