# Cal or No Cal? -- Real-Time Miscalibration Detection of LiDAR and Camera Sensors

arXiv: https://arxiv.org/abs/2504.01040 · HTML: https://mlsanity.com/paper/2504.01040/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 39 references that are in the map.
- First posted on arXiv 31 Mar 2025.

## Builds on (most cited first, 17 of 17)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Similarity of Neural Network Representations Revisited](https://mlsanity.com/paper/1905.00414/) (2019)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation](https://mlsanity.com/paper/2205.13542/) (2022)
- [Self-Supervised Learning of Pretext-Invariant Representations](https://mlsanity.com/paper/1912.01991/) (2019)
- [Do Vision Transformers See Like Convolutional Neural Networks?](https://mlsanity.com/paper/2108.08810/) (2021)
- [TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers](https://mlsanity.com/paper/2203.11496/) (2022)
- [Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving](https://mlsanity.com/paper/2303.11040/) (2023)
- [SparseFusion: Fusing Multi-Modal Sparse Representations for Multi-Sensor 3D Object Detection](https://mlsanity.com/paper/2304.14340/) (2023)
- [RegNet: Multimodal Sensor Registration Using Deep Neural Networks](https://mlsanity.com/paper/1707.03167/) (2017)
- [Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection](https://mlsanity.com/paper/2205.14951/) (2022)
- [Pixel-level Extrinsic Self Calibration of High Resolution LiDAR and Camera in Targetless Environments](https://mlsanity.com/paper/2103.01627/) (2021)
- [GraphAlign: Enhancing Accurate Feature Alignment by Graph matching for Multi-Modal 3D Object Detection](https://mlsanity.com/paper/2310.08261/) (2023)
- [CRLF: Automatic Calibration and Refinement based on Line Feature for LiDAR and Camera in Road Scenes](https://mlsanity.com/paper/2103.04558/) (2021)
- [SOAC: Spatio-Temporal Overlap-Aware Multi-Sensor Calibration using Neural Radiance Fields](https://mlsanity.com/paper/2311.15803/) (2023)
- [Learned Fusion: 3D Object Detection using Calibration-Free Transformer Feature Fusion](https://mlsanity.com/paper/2312.09082/) (2023)

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

- [FlowCalib: LiDAR-to-Vehicle Miscalibration Detection using Scene Flows](https://mlsanity.com/paper/2601.23107/) (2026)
- [Adversarial Calibration Attack on Autonomous Vehicles](https://mlsanity.com/paper/2608.28778/) (2026)

## Abstract (arXiv)

The goal of extrinsic calibration is the alignment of sensor data to ensure an accurate representation of the surroundings and enable sensor fusion applications. From a safety perspective, sensor calibration is a key enabler of autonomous driving. In the current state of the art, a trend from target-based offline calibration towards targetless online calibration can be observed. However, online calibration is subject to strict real-time and resource constraints which are not met by state-of-the-art methods. This is mainly due to the high number of parameters to estimate, the reliance on geometric features, or the dependence on specific vehicle maneuvers. To meet these requirements and ensure the vehicle's safety at any time, we propose a miscalibration detection framework that shifts the focus from the direct regression of calibration parameters to a binary classification of the calibration state, i.e., calibrated or miscalibrated. Therefore, we propose a contrastive learning approach that compares embedded features in a latent space to classify the calibration state of two different sensor modalities. Moreover, we provide a comprehensive analysis of the feature embeddings and challenging calibration errors that highlight the performance of our approach. As a result, our method outperforms the current state-of-the-art in terms of detection performance, inference time, and resource demand. The code is open source and available on https://github.com/TUMFTM/MiscalibrationDetection.