# Neural Geometric Parser for Single Image Camera Calibration

arXiv: https://arxiv.org/abs/2007.11855 · HTML: https://mlsanity.com/paper/2007.11855/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2020.

## Built on by (most cited first, 13 of 13)

- [Perspective Fields for Single Image Camera Calibration](https://mlsanity.com/paper/2212.03239/) (2022)
- [CTRL-C: Camera calibration TRansformer with Line-Classification](https://mlsanity.com/paper/2109.02259/) (2021)
- [Deep Learning for Camera Calibration and Beyond: A Survey](https://mlsanity.com/paper/2303.10559/) (2023)
- [Thinking with Camera: A Unified Multimodal Model for Camera-Centric Understanding and Generation](https://mlsanity.com/paper/2510.08673/) (2025)
- [DiffCalib: Reformulating Monocular Camera Calibration as Diffusion-Based Dense Incident Map Generation](https://mlsanity.com/paper/2405.15619/) (2024)
- [Rethinking Generic Camera Models for Deep Single Image Camera Calibration to Recover Rotation and Fisheye Distortion](https://mlsanity.com/paper/2111.12927/) (2021)
- [A Reliable Online Method for Joint Estimation of Focal Length and Camera Rotation](https://mlsanity.com/paper/2207.12934/) (2022)
- [RotBench: Evaluating Multimodal Large Language Models on Identifying Image Rotation](https://mlsanity.com/paper/2508.13968/) (2025)
- [Deep Single Image Camera Calibration by Heatmap Regression to Recover Fisheye Images Under Manhattan World Assumption](https://mlsanity.com/paper/2303.17166/) (2023)
- [SOFI: Multi-Scale Deformable Transformer for Camera Calibration with Enhanced Line Queries](https://mlsanity.com/paper/2409.15553/) (2024)
- [Estimating Extreme 3D Image Rotation with Transformer Cross-Attention](https://mlsanity.com/paper/2303.02615/) (2023)
- [CoL3D: Collaborative Learning of Single-view Depth and Camera Intrinsics for Metric 3D Shape Recovery](https://mlsanity.com/paper/2502.08902/) (2025)
- [Puffin-World: Scaling a Unified Multimodal Model with Native 3D World States](https://mlsanity.com/paper/2609.04196/) (2026)

## Abstract (arXiv)

We propose a neural geometric parser learning single image camera calibration for man-made scenes. Unlike previous neural approaches that rely only on semantic cues obtained from neural networks, our approach considers both semantic and geometric cues, resulting in significant accuracy improvement. The proposed framework consists of two networks. Using line segments of an image as geometric cues, the first network estimates the zenith vanishing point and generates several candidates consisting of the camera rotation and focal length. The second network evaluates each candidate based on the given image and the geometric cues, where prior knowledge of man-made scenes is used for the evaluation. With the supervision of datasets consisting of the horizontal line and focal length of the images, our networks can be trained to estimate the same camera parameters. Based on the Manhattan world assumption, we can further estimate the camera rotation and focal length in a weakly supervised manner. The experimental results reveal that the performance of our neural approach is significantly higher than that of existing state-of-the-art camera calibration techniques for single images of indoor and outdoor scenes.