mlsanityData to 14 Oct 2026

Array Camera Image Fusion using Physics-Aware Transformers

arXiv 2207.02250 ↗First posted 5 Jul 2022cs.CV eess.IVcited by 1

Not on the map. Why?

Abstract (arXiv)

We demonstrate a physics-aware transformer for feature-based data fusion from cameras with diverse resolution, color spaces, focal planes, focal lengths, and exposure. We also demonstrate a scalable solution for synthetic training data generation for the transformer using open-source computer graphics software. We demonstrate image synthesis on arrays with diverse spectral responses, instantaneous field of view and frame rate.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 37 references that are in the map. First posted on arXiv 5 Jul 2022.

Builds on

11 of 37 references.

References · 11

  1. 2017Attention Is All You Needcited by 60,361
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2015Spatial Transformer Networkscited by 1,429
  5. 2017Deep Image Priorcited by 1,098
  6. 2015FlowNet: Learning Optical Flow with Convolutional Networkscited by 1,059
  7. 2015A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimationcited by 881
  8. 2016The Little Engine that Could: Regularization by Denoising (RED)cited by 307
  9. 2017Benchmarking Denoising Algorithms with Real Photographscited by 209
  10. 2020Epipolar Transformerscited by 117
  11. 2019Learning Parallax Attention for Stereo Image Super-Resolutioncited by 56

Built on by

Cited by year

Citing papers per year: 2024: 1.

1 paper.