Formalizing Linear Motion G-code for Invariant Checking and Differential Testing of(…)
summary
Formalizes G‑code as sets of cuboids then samples them into point clouds and implements an efficient differential checker called GlitchFinder; prototype compares slicers (Cura vs PrusaSlicer) and mesh tools (MeshLab, Meshmixer), produces per‑box heat maps, and runs on 58 real‑world CAD models. Shows concrete results: it flags 56 buggy models, highlights tiny feature failures that mesh repair misses, and surfaces slicer discrepancies—tool and evaluation are implemented and artifacts are available.
AI-written summary · about
chapters (9)
- 0:003D printing software failures
- 1:28G-code analysis for bug detection
- 3:32Differential testing of G-code
- 4:36Formalizing G-code semantics
- 6:43Point cloud comparison
- 8:28Detecting model bugs with Glitchfinder
- 10:14Finding slicer and repair bugs
- 11:17Conclusion and future
- 11:37Q&A
related talks
16mGeometry Types for Graphics Programming
15mSpecifying and Testing GPU Workgroup Progress Models
01h 03mMaking Hard C++ Tests Easy: A Case Study From the Motion Planning Domain
23mVerified Code Generation for the Polyhedral Model
20mGIGS, open-source geodetic testing to verify your spatial data and applications
27mIEEE CoG 2019 - Industry Day - Hubert Mohr-Daurat (Creative Assembly)
24mTesting Techniques for Critical Deep Learning Applications
25mExploring the Universal Library