Reproduce “Deep Learning for Camera Calibration and Beyond: A Survey” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Kang Liao, Lang Nie, Shujuan Huang, Chunyu Lin, Jing Zhang, Yao Zhao, Moncef Gabbouj, Dacheng Tao
The authors survey learning-based camera calibration, organizing more than 200 papers into standard pinhole, distortion, cross-view, and cross-sensor approaches. They distinguish methods that regress camera parameters from those that learn image mappings, and present a dataset of synthetic and real-world data. Their evaluation reports performance degradation in cross-domain settings, so the resource does not establish universal generalization.
Key insights
- Learning-based calibration is shifting from direct parameter regression towards leveraging intermediate geometric representations and pixel-level mapping functions.
- Self-supervised and unsupervised learning strategies are increasingly adopted to mitigate the high cost of data labeling in calibration tasks.
- There is a growing trend of integrating camera calibration with other 3D vision tasks, such as SLAM and NeRF, for more robust and refined scene understanding.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.