mlsanityData to 14 Oct 2026

Event Cameras for Melt-Pool Monitoring in Additive Manufacturing: A Benchmark and a Cross-Machine Transfer Analysis

arXiv 2610.06973 ↗First posted 3 Oct 2026cs.CVcited by 0

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Abstract (arXiv)

Melt-pool monitoring is central to qualifying metal additive manufacturing (AM), yet no public event-camera benchmark exists for this domain. Event cameras report per-pixel brightness changes with microsecond timing instead of reading full frames, giving the temporal resolution AM transients demand at a fraction of the data rate. We present SynAM-E (Synthetic AM Events), the first public multi-source simulated event-camera benchmark for metal-AM melt-pool monitoring: 85 physics-calibrated event shards from 15 sources across 8 institutions, with public baselines and fixed cross-machine evaluation splits. On a single-machine case study, event-spatial monitoring matches dense-frame accuracy (0.874 versus 0.863 macro-F1), and the absolute intensity that events discard adds only +0.006 under fusion. On the NIST Additive Manufacturing Metrology Testbed (AMMT) build, a near-sensor event-rate counter recovers a raw-frame-confirmed 528.7 Hz intensity oscillation at ~380 times less sensor readout than the frame stream requires. A compact 93 k-parameter spiking model runs at 15 times lower modeled inference energy for a 0.073 macro-F1 cost. Every cross-source task includes a built-in trust test against camera identity shortcuts: process-type classification passes while material classification remains confounded by camera band, a corpus-structural limitation the release documents and the trust test exposes.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 32 of its 81 references that are in the map. First posted on arXiv 3 Oct 2026.

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32 of 81 references.

References · 32

  1. 2020Shortcut Learning in Deep Neural Networkscited by 1,969
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  7. 2015Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccadescited by 314
  8. 2021DSEC: A Stereo Event Camera Dataset for Driving Scenarioscited by 303
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  10. 2018EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Camerascited by 224
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20 more
  1. 2018Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Carscited by 200
  2. 2020Learning to Detect Objects with a 1 Megapixel Event Cameracited by 186
  3. 2023SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligencecited by 169
  4. 2019Video to Events: Recycling Video Datasets for Event Camerascited by 165
  5. 2022Recurrent Vision Transformers for Object Detection with Event Camerascited by 159
  6. 2020Event-based Asynchronous Sparse Convolutional Networkscited by 104
  7. 2024Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chipscited by 103
  8. 2021N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Camerascited by 92
  9. 2024Recent Event Camera Innovations: A Surveycited by 68
  10. 2022Event Transformer. A sparse-aware solution for efficient event data processingcited by 50
  11. 2023V2CE: Video to Continuous Events Simulatorcited by 25
  12. 2024Navigating Shortcuts, Spurious Correlations, and Confounders: From Origins via Detection to Mitigationcited by 25
  13. 2023Shining light on the DVS pixel: A tutorial and discussion about biasing and optimizationcited by 23
  14. 2021Feedback control of event camerascited by 21
  15. 2023Optimal biasing and physical limits of DVS event noisecited by 14
  16. 2024ADV2E: Bridging the Gap Between Analogue Circuit and Discrete Frames in the Video-to-Events Simulatorcited by 8
  17. 2021Statistical quantification of confounding bias in predictive modellingcited by 7
  18. 2025How Real is CARLAs Dynamic Vision Sensor? A Study on the Sim-to-Real Gap in Traffic Object Detectioncited by 7
  19. 2024Autobiasing Event Camerascited by 3
  20. 2024Investigation on domain adaptation of additive manufacturing monitoring systems to enhance digital twin reusabilitycited by 2

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