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DTSTAMP:20260805T205605Z
LOCATION:Room 403  B
DTSTART;TZID=America/Los_Angeles:20260720T162500
DTEND;TZID=America/Los_Angeles:20260720T163500
UID:siggraph_SIGGRAPH 2026_sess118_papers_474@linklings.com
SUMMARY:DeepMill++: Neural Guidance Meets Rasterization for Efficient Acce
 ssibility Analysis
DESCRIPTION:Fanchao Zhong, Yao Zhang, and Guanze Xin (Shandong University)
 ; Peng-Shuai Wang (Peking University); and Lin Lu, Changhe Tu, and Haisen 
 Zhao (Shandong University)\n\nWe introduce DeepMill++, a conservative and 
 highly efficient framework for cutter accessibility analysis on arbitrary 
 triangular meshes. DeepMill++ reformulates accessibility and occlusion det
 ection as a rasterization-based visibility and depth pooling problem. Deep
 Mill++ achieves up to 9.5× speedup over state-of-the-art geometric methods
 , while maintaining 97.5% conservative accessibility accuracy.\n\nInterest
  Area: Research & Education\n\nKeyword: Artificial Intelligence/Machine Le
 arning, Fabrication, Geometry, Modeling, Simulation\n\nRegistration Catego
 ry: Full Conference Supporter, Full Conference\n\nSession Chair: Mina Kona
 kovic Lukovic (Massachusetts Institute of Technology (MIT))\n\n
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