Instant NGP를 활용한 CNC Tool의 장면 생성 및 렌더링 성능 평가
Scene Generation of CNC Tools Utilizing Instant NGP and Rendering Performance Evaluation
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초록
CNC tools contribute to the production of high-precision and consistent results. However, employing damaged CNC tools or utilizing compromised numerical control can lead to significant issues, including equipment damage, overheating, and system-wide errors. Typically, the assessment of external damage to CNC tools involves capturing a single viewpoint through a camera to evaluate tool wear. This study aims to enhance existing methods by using only a single manually focused Microscope camera to enable comprehensive external analysis from multiple perspectives. Applying the NeRF (Neural Radiance Fields) algorithm to images captured with a single manual focus microscope camera, we construct a 3D rendering system. Through this system, it is possible to generate scenes of areas that cannot be captured even with a fixed camera setup, thereby assisting in the analysis of exterior features. However, the NeRF model requires considerable training time, ranging from several hours to over two days. To overcome these limitations of NeRF, various subsequent models have been developed. Therefore, this study aims to compare and apply the performance of Instant NGP, Mip-NeRF, and DS-NeRF, which have garnered attention following NeRF.
Abstract
CNC tools contribute to the production of high-precision and consistent results. However, employing damaged CNC tools or utilizing compromised numerical control can lead to significant issues, including equipment damage, overheating, and system-wide errors. Typically, the assessment of external damage to CNC tools involves capturing a single viewpoint through a camera to evaluate tool wear. This study aims to enhance existing methods by using only a single manually focused Microscope camera to enable comprehensive external analysis from multiple perspectives. Applying the NeRF (Neural Radiance Fields) algorithm to images captured with a single manual focus microscope camera, we construct a 3D rendering system. Through this system, it is possible to generate scenes of areas that cannot be captured even with a fixed camera setup, thereby assisting in the analysis of exterior features. However, the NeRF model requires considerable training time, ranging from several hours to over two days. To overcome these limitations of NeRF, various subsequent models have been developed. Therefore, this study aims to compare and apply the performance of Instant NGP, Mip-NeRF, and DS-NeRF, which have garnered attention following NeRF.
참고문헌 (14)
- S. J. Kim, "Auto-encoder Based Incipient Anomaly Detection of CNC Milling Tool Considering Machining Tool Path", Proceeding of KSPE, pp.377~377 (2023)
- J. D. Choi, "Dynamic 3D Worker Pose Registration for Safety Monitoring in Manufacturing Environment based on Multi-domain Vision System", IEMEK, 18(6), pp.303~310 (2023)
- B. Mildenhall, "Nerf : Representing Scenes as Neural Radiance Fields for View Synthesis", Communications of the ACM, 65(1), pp.99~106 (2021)
- W. S. Kim, "Development of Auto-spray system to improve the quality of 3D Scanning Quality", Journal of the Korea Academia-Industrial Cooperation Society, 17(4), pp.100~105 (2016)
- D. Y. Um, "The Reflected Property Analysis of 3D Laser Scanning System as Object Surface Materials", Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 27(3), pp.347~356 (2009)
- T. M¨uller, "Instant Neural Graphics Primitives with a Multiresolution Hash Encoding", ACM Transactions on Graphics(ToG), 41(4), pp.1~15 (2022)
- N. Kanopoulos, "Design of an Image Edge Detection Filter Using the Sobel Operator", IEEE Journal of Solid-state Circuits, 23(2), pp.358~367 (1988)
- J. H. Jeon, "Subject Region-Based Auto-Focusing Algorithm Using Noise Robust Focus Measure", Journal of the Institute of Electronics Engineers of Korea, 48(2), pp.80~87 (2011)
- J. T. Barron, "Mip-nerf: A Multiscale Representation for Anti-aliasing Neural Radiance Fields", Proceedings of the IEEE, 2021, 5855~5864
- K. Deng, "Depth-supervised Nerf: Fewer Views and Faster Rraining for Free", Proceedings of the IEEE, 2022, pp.2, 12882~12891
- A. Horé, "Image Quality Metrics: PSNR vs. SSIM", 20th International Conference on Pattern Recognition, pp.2366~2369 (2010)
- Z. Wang, "Image Quality Assessment : From Error Visibility to Structural Similarity", IEEE Transactions on Image Processing, 13(4), pp.600~612 (2004)
- R. Zhang, "The Unreasonable Effectiveness of Deep Features as a Perceptual Metric", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.586~595 (2018)
- J. Yang, "Visual Anomaly Detection for Images : a Systematic Survey", Procedia Computer Science, 199(61), pp.471~478 (2022)