SIGGRAPH Asia 2025最佳论文 | 港中大、曼彻斯特大学获奖

Core Insights - SIGGRAPH Asia is a leading conference in computer graphics and 3D visualization, showcasing the latest breakthroughs in the field, with 1,106 technical papers submitted for the 2025 review, resulting in 201 conference papers and 100 journal papers accepted, including only 5 Best Paper Awards [2] - The rise of consumer-grade 3D printing has shifted focus from merely generating aesthetically pleasing 3D models to ensuring their manufacturability in the real world, highlighting the importance of practical applications [5] - The Best Paper Award at SIGGRAPH Asia 2025 was awarded to a study on a new slicing framework for multi-axis DLP 3D printing, which optimizes the slicing process using mathematical tools from neural network training [6] Group 1 - The study introduces a novel slicing framework that redefines the DLP 3D printing process, utilizing a continuous trajectory optimization approach to improve the manufacturing of complex geometries without support structures [6][7] - Traditional DLP 3D printing faces physical limitations due to fixed planar slicing, leading to challenges such as the need for support structures and visible layer lines on printed models [10][11] - The research proposes a multi-axis concept that allows for the adjustment of the build platform's angle, enabling smoother surfaces and reducing the need for support structures [11] Group 2 - The core contribution of the study is the transformation of the slicing problem into a continuous mathematical optimization problem, moving away from discrete geometric rules [14][50] - The optimization framework incorporates both soft objectives, such as surface quality, and hard constraints, ensuring physical feasibility during the printing process [24][27] - The algorithm demonstrates high convergence efficiency, with most test cases generating trajectories in under 30 seconds, showcasing its practical applicability [44] Group 3 - The research team implemented advanced strategies, including joint optimization of the initial pose of the model and adaptive multi-curve segmentation, to enhance the algorithm's solving capabilities for complex geometries [32][39] - The physical experiments validated the manufacturability and surface quality of the generated trajectories, confirming the effectiveness of the proposed optimization framework [48][53] - The study emphasizes the potential for numerical optimization methods to revolutionize manufacturing process planning, with implications for other fields such as CNC machining and robotic welding [52][56]