# CCDN: Checkerboard Corner Detection Network for Robust Camera Calibration

arXiv: https://arxiv.org/abs/2302.05097 · HTML: https://mlsanity.com/paper/2302.05097/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 10 Feb 2023.

## Builds on (most cited first, 4 of 4)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks](https://mlsanity.com/paper/1312.6229/) (2013)
- [ChESS - Quick and Robust Detection of Chess-board Features](https://mlsanity.com/paper/1301.5491/) (2013)

## Built on by (most cited first, 2 of 2)

- [PuzzleBoard: A New Camera Calibration Pattern with Position Encoding](https://mlsanity.com/paper/2409.20127/) (2024)
- [RCDN -- Robust X-Corner Detection Algorithm based on Advanced CNN Model](https://mlsanity.com/paper/2307.03505/) (2023)

## Abstract (arXiv)

Aiming to improve the checkerboard corner detection robustness against the images with poor quality, such as lens distortion, extreme poses, and noise, we propose a novel detection algorithm which can maintain high accuracy on inputs under multiply scenarios without any prior knowledge of the checkerboard pattern. This whole algorithm includes a checkerboard corner detection network and some post-processing techniques. The network model is a fully convolutional network with improvements of loss function and learning rate, which can deal with the images of arbitrary size and produce correspondingly-sized output with a corner score on each pixel by efficient inference and learning. Besides, in order to remove the false positives, we employ three post-processing techniques including threshold related to maximum response, non-maximum suppression, and clustering. Evaluations on two different datasets show its superior robustness, accuracy and wide applicability in quantitative comparisons with the state-of-the-art methods, like MATE, ChESS, ROCHADE and OCamCalib.