Transformer-based methods have become the dominant approach for 3D instance segmentation. These methods predict instance masks via instance queries, ranking them by classification confidence and IoU scores to select the top prediction as the final outcome. However, it has been observed that the current models employ a fixed and higher number of queries than the instances present within a scene. In such instances, multiple queries predict the same instance, yet only a single query is ultimately optimized. The close scores of queries in the lower-level decoders make it challenging for the dominant query to distinguish itself rapidly, which ultimately impairs the model's accuracy and convergence efficiency. This phenomenon is referred to as inter-query competition. To address this challenge, we put forth a series of plug-and-play competition-oriented designs, collectively designated as the CompetitorFormer, with the aim of reducing competition and facilitating a dominant query. Experiments showed that integrating our designs with state-of-the-art frameworks consistently resulted in significant performance improvements in 3D instance segmentation across a range of datasets.

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.