Automatic calibration of multi-camera systems, namely the accurate estimation of spatial extrinsic parameters, is fundamental for 3D reconstruction, panoramic perception, and multi-view data fusion. Existing methods typically rely on calibration targets, explicit geometric modeling, or task-specific neural networks. Such approaches often exhibit limited robustness and applicability in complex dynamic environments or online scenarios, making them difficult to deploy in practical applications. To address this, this paper proposes GMAC, a multi-camera extrinsic estimation framework based on the implicit geometric representations learned by multi-view reconstruction networks. GMAC models extrinsics as global variables constrained by the latent multi-view geometric structure and prunes and structurally reconfigures existing networks so that their latent features can directly support extrinsic prediction through a lightweight regression head, without requiring a completely new network design. Furthermore, GMAC jointly optimizes cross-view reprojection consistency and multi-view cycle consistency, ensuring geometric coherence across cameras while improving prediction accuracy and optimization stability. Experiments on both synthetic and real-world multi-camera datasets demonstrate that GMAC achieves accurate and stable extrinsic estimation without explicit 3D reconstruction or manual calibration, providing a new solution for efficient deployment and online calibration of multi-camera systems.
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；FZI Research Center for Information Technology（FZI信息技术研究中心）
In this paper, we present TEScalib, a novel extrinsic self-calibration approach of LiDAR and stereo camera using the geometric and photometric information of surrounding environments without any calibration targets for automated driving vehicles. Since LiDAR and stereo camera are widely used for sensor data fusion on automated driving vehicles, their extrinsic calibration is highly important. However, most of the LiDAR and stereo camera calibration approaches are mainly target-based and therefore time consuming. Even the newly developed targetless approaches in last years are either inaccurate…
Mobile robotic applications need precise information about the geometric position of the individual sensors on the platform. This information is given by the extrinsic calibration parameters which define how the sensor is rotated and translated with respect to a fixed reference coordinate system. Erroneous calibration parameters have a negative impact on typical robotic estimation tasks, e.g. SLAM. In this work we propose a new method for a continuous estimation of the calibration parameters during operation of the robot. The parameter estimation is based on the matching of point clouds which …
CalibNet: Geometrically Supervised Extrinsic Calibration using 3D Spatial Transformer Networks
International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术学院）；Mila（米拉研究所）；Université de Montréal（蒙特利尔大学）
3D LiDAR和2D相机越来越多地在传感器设备中同时使用，以执行感知任务。然而，在这些传感器可以用于收集有意义的数据之前，它们的外部（和内部）需要被精确校准，因为传感器装置的性能对这些校准参数非常敏感。绝大多数现有的校准技术需要大量的数据和/或校准目标和人力，严重影响了它们在大规模生产系统中的适用性。我们通过CalibNet解决了这一差距：一个自监督的深度网络，能够实时自动估计3D LiDAR和2D相机之间的6自由度刚体变换。CalibNet减少了对校准目标的需求，从而大大节省了校准工作。在训练过程中，网络仅将LiDAR点云、相应的单目图像和相机校准矩阵K作为输入。在训练时，我们不进行直接监督（即，例如，我们不直接回归到校准参数）。相反，我们训练网络来预测校准参数，以最大限度地提高输入图像和点云的几何和光度一致性。CalibNet学习迭代解决潜在的几何问题，并准确预测各种错误校准的外部校准参数，而无需重新训练或领域自适应。该项目的页面位于https://epiception.github.io/CalibNet
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Hong Kong University of Science and Technology（香港科技大学）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深港协同创新研究院）；CWB Institute of Autonomous Driving（CWB自动驾驶研究院）
The combination of LiDARs and cameras enables a mobile robot to perceive environments with multi-modal data, becoming a key factor in achieving robust perception. Traditional frame cameras are sensitive to changing illumination conditions, motivating us to introduce novel event cameras to make LiDAR-camera fusion more complete and robust. However, to jointly exploit these sensors, the challenging extrinsic calibration problem should be addressed. This paper proposes an automatic checkerboard-based approach to calibrate extrinsics between a LiDAR and a frame/event camera, where four contributio…
YOCO: You Only Calibrate Once for Accurate Extrinsic Parameter in LiDAR-Camera Systems
China University of Mining and Technology (Beijing)（中国矿业大学（北京））；College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing)（中国矿业大学（北京）地球科学与测绘工程学院）
In this paper, we propose a novel online self-calibration approach for Light Detection and Ranging (LiDAR) and camera sensors. Compared to the previous CNN-based methods that concatenate the feature maps of the RGB image and decalibrated depth image, we exploit the cost volume inspired by the PWC-Net for feature matching. Besides the smooth L1-Loss of the predicted extrinsic calibration parameters, an additional point cloud loss is applied. Instead of regress the extrinsic parameters between LiDAR and camera directly, we predict the decalibrated deviation from initial calibration to the ground…
Department of Control Science & Engineering, the College of Electronic & Information Engineering, Tongji University（控制科学与工程系，电子与信息工程学院，同济大学）；School of Computer Science and Engineering, Central South University（计算机科学与工程学院，中南大学）；Beijing Institute of Aerospace Control Devices（北京航天控制器件研究所）；Institute of Automation, Chinese Academy of Sciences（自动化研究所，中国科学院）
Targetless LiDAR-Camera Calibration with Neural Gaussian Splatting
Department of Interdisciplinary Program in Artificial Intelligence, Seoul National University（人工智能交叉学科项目系，首尔国立大学）；Department of Artificial Intelligence, Yonsei University（人工智能系，延世大学）
Multi-Cali Anything: Dense Feature Multi-Frame Structure-from-Motion for Large-Scale Camera Array Calibration
School of Computer Science, Carnegie Mellon University（计算机科学系，卡内基梅隆大学）；Electrical and Computer Engineering, Carnegie Mellon University（电气与计算机工程，卡内基梅隆大学）；Harvard John A. Paulson School of Engineering And Applied Sciences, Harvard University（哈佛大学约翰·A·保罗森工程与应用科学学院）；Department of Electrical and Electronic Engineering, The University of Hong Kong（香港大学电子与电气工程系）；School of Mathematical Science, University of Nottingham（诺丁汉大学数学科学学院）；Weill Cornell Medicine, Cornell University（韦尔医学院，康奈尔大学）；Meta Reality Labs（Meta现实实验室）
LiDAR-Camera Calibration using 3D-3D Point correspondences
Vellore Institute of Technology, Chennai（金奈韦洛尔理工大学）；Birla Institute of Technology and Science, Hyderabad（海得拉巴比尔拉理工学院）；Veermata Jijabai Technological Institute, Mumbai（孟买韦尔马塔·吉贾拜技术学院）；International Institute of Information and Technology, Hyderabad（海得拉巴国际信息技术学院）
For autonomous vehicles, an accurate calibration for LiDAR and camera is a prerequisite for multi-sensor perception systems. However, existing calibration techniques require either a complicated setting with various calibration targets, or an initial calibration provided beforehand, which greatly impedes their applicability in large-scale autonomous vehicle deployment. To tackle these issues, we propose a novel method to calibrate the extrinsic parameter for LiDAR and camera in road scenes. Our method introduces line features from static straight-line-shaped objects such as road lanes and pole…
In this paper, we propose a method for online extrinsic camera calibration, i.e., estimating pitch, yaw, roll angles and camera height from road surface in sequential driving scene images. The proposed method estimates the extrinsic camera parameters in two steps: 1) pitch and yaw angles are estimated simultaneously using a vanishing point computed from a set of lane boundary observations, and then 2) roll angle and camera height are computed by minimizing difference between lane width observations and a lane width prior. The extrinsic camera parameters are sequentially updated using extended …
CLRNet: Targetless Extrinsic Calibration for Camera, Lidar and 4D Radar Using Deep Learning
Hungarian Research Network Institute for Computer Science and Control (HUN-REN SZTAKI)（匈牙利研究网络计算机科学与控制研究所）；Pázmány Péter Catholic University（帕兹曼·佩斯特天主教大学）；Intelligent Vehicles Group, TU Delft（智能车辆组，代尔夫特理工大学）；Perciv AI
Uncertainty-Aware Online Extrinsic Calibration: A Conformal Prediction Approach
Université de technologie de Compiègne, CNRS, Heudiasyc（法国图卢兹工学院、CNRS、Heudiasyc）；CNRS, INSA Lyon, UCBL, LIRIS, UMR5205（CNRS、里昂国立应用科学学院、UCBL、LIRIS、UMR5205）
Iterative Camera-LiDAR Extrinsic Optimization via Surrogate Diffusion
School of Automation, Beijing Institute of Technology（自动化学院，北京理工大学）；Centre for Robotics Research, Department of Engineering, King’s College London（机器人研究中心，工程系，伦敦大学国王学院）；School of Integrated Circuits and Electronics, Beijing Institute of Technology（集成电路与电子学院，北京理工大学）
Monocular camera sensors are vital to intelligent vehicle operation and automated driving assistance and are also heavily employed in traffic control infrastructure. Calibrating the monocular camera, though, is time-consuming and often requires significant manual intervention. In this work, we present an extrinsic camera calibration approach that automatizes the parameter estimation by utilizing semantic segmentation information from images and point clouds. Our approach relies on a coarse initial measurement of the camera pose and builds on lidar sensors mounted on a vehicle with high-precisi…
As neuromorphic technology is maturing, its application to robotics and autonomous vehicle systems has become an area of active research. In particular, event cameras have emerged as a compelling alternative to frame-based cameras in low-power and latency-demanding applications. To enable event cameras to operate alongside staple sensors like lidar in perception tasks, we propose a direct, temporally-decoupled extrinsic calibration method between event cameras and lidars. The high dynamic range, high temporal resolution, and low-latency operation of event cameras are exploited to directly regi…
Camera calibration for the surround-view system: a benchmark and dataset
Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学研究所）；Beijing Key Laboratory of Advanced Information Science and Network（北京先进信息科学与网络重点实验室）
Calibration of multi-camera systems, i.e. determining the relative poses between the cameras, is a prerequisite for many tasks in computer vision and robotics. Camera calibration is typically achieved using offline methods that use checkerboard calibration targets. These methods, however, often are cumbersome and lengthy, considering that a new calibration is required each time any camera pose changes. In this work, we propose a novel, marker-free online method for the extrinsic calibration of multiple smart edge sensors, relying solely on 2D human keypoint detections that are computed locally…
Native-Domain Cross-Attention for Camera-LiDAR Extrinsic Calibration Under Large Initial Perturbations
School of Automation, Beijing Institute of Technology（北京理工大学自动化学院）；School of Integrated Circuits and Electronics, Beijing Institute of Technology（北京理工大学集成电路与电子学院）
A Target-based Multi-LiDAR Multi-Camera Extrinsic Calibration System
Research \& Development, Toyota Material Handling Manufacturing, Bologna Italy Department of Information Engineering, University of Pisa, Pisa, Italy School of Engineering；Materials Science, Queen Mary University of London, London, United Kingdom
Handong Global University（韩东国际大学）；Virginia Tech（弗吉尼亚理工大学）
This paper presents a novel automatic calibration system to estimate the extrinsic parameters of LiDAR mounted on a mobile platform for sensor misalignment inspection in the large-scale production of highly automated vehicles. To obtain subdegree and subcentimeter accuracy levels of extrinsic calibration, this study proposed a new concept of a target board with embedded photodetector arrays, named the PD-target system, to find the precise position of the correspondence laser beams on the target surface. Furthermore, the proposed system requires only the simple design of the target board at the…
Hong Kong University of Science and Technology（香港科技大学）
Combining multiple LiDARs enables a robot to maximize its perceptual awareness of environments and obtain sufficient measurements, which is promising for simultaneous localization and mapping (SLAM). This paper proposes a system to achieve robust and simultaneous extrinsic calibration, odometry, and mapping for multiple LiDARs. Our approach starts with measurement preprocessing to extract edge and planar features from raw measurements. After a motion and extrinsic initialization procedure, a sliding window-based multi-LiDAR odometry runs onboard to estimate poses with online calibration refine…
We propose a generic event camera calibration framework using image reconstruction. Instead of relying on blinking LED patterns or external screens, we show that neural-network-based image reconstruction is well suited for the task of intrinsic and extrinsic calibration of event cameras. The advantage of our proposed approach is that we can use standard calibration patterns that do not rely on active illumination. Furthermore, our approach enables the possibility to perform extrinsic calibration between frame-based and event-based sensors without additional complexity. Both simulation and real…
