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However, this disjointed approach lacks interoperability and fails to fully unleash the potential of a more integrated, multitask solution. To overcome this limitation, we introduce TUS-Net, an innovative transformer-based architecture meticulously crafted for both semantic and instance segmentation of point clouds. Our model introduces two pivotal advancements: First, it employs a superpoint-based pre-processing step that minimizes computational overhead without compromising on precision. Second, we leverage a dual-branch design within the transformer architecture, allowing it to adapt to the nuances of both segmentation tasks dynamically. Through extensive experimentation on the ScanNet dataset, our findings demonstrate that TUS-Net surpasses prevailing specialized models by a substantial margin and maintains remarkable computational efficiency. Notably, we achieve a 5.7% enhancement in mean Average Precision (mAP), for instance, segmentation, while striking an optimal balance between accuracy and runtime for semantic segmentation. These outcomes underscore the versatility, efficiency and high-performance attributes of TUS-Net, positioning it as an indispensable framework for robust 3D point cloud segmentation. <\/jats:p>","DOI":"10.1142\/s0218126624501482","type":"journal-article","created":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T09:28:34Z","timestamp":1700731714000},"source":"Crossref","is-referenced-by-count":0,"title":["Transformer-Based Unified Segmentation: Simultaneous Semantic and Instance Tasks on Point Clouds"],"prefix":"10.1142","volume":"33","author":[{"given":"Lei","family":"Pan","sequence":"first","affiliation":[{"name":"School of Computer Science, Civil Aviation Flight University of China, P. R. 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