DebrisTracer: Reliable Tracking in Hypervelocity Impact Fast Imaging
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Computer Science > Machine Learning
Title:DebrisTracer: Reliable Tracking in Hypervelocity Impact Fast Imaging
Abstract:This application paper presents DebrisTracer, a framework for the reliable tracking of debris in hypervelocity impact fast imaging. These noisy and highly specific datasets capture the ejection of a large number of debris fragments after the impact of a projectile launched at hypervelocity into a target material. The reliable estimation of debris mass and speed distributions is of major importance in aerospace applications. We document how to extend an off-the-shelf topology tracking framework based on critical point extraction and matching, in order to incorporate domain knowledge and physical assumptions. Our approach automatically produces an accurate and reliable debris tracking, enabling an interpretable visual analysis of this complex space-time phenomenon. Extensive experiments demonstrate the accuracy improvements provided by our approach over established tools used by domain experts in terms of physical validation, specifically via the prediction of the experimental ejected mass and crater depth profiles. We illustrate the utility of our approach across several use cases (with varying impact angles and physics). We show that our statistical summaries enable the visual identification of distinct regimes within the debris population, corroborating and refining prior expectations of domain experts. Our database and our C++ implementation are available at this address: this https URL.
| Comments: | 14 pages (12 + appendix), 16 figures. Accepted at IEEE VIS 2026. To appear in IEEE Transactions on Visualization and Computer Graphics (TVCG) |
| Subjects: | Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Image and Video Processing (eess.IV) |
| Cite as: | arXiv:2607.15986 [cs.LG] |
| (or arXiv:2607.15986v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.15986
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Théophane Loloum [view email][v1] Fri, 17 Jul 2026 14:24:05 UTC (7,159 KB)
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