Tensor-Based Self-Calibration of Cameras via the TrifocalCalib Method

• URL: http://arxiv.org/abs/2509.17620v1
• Date: Mon, 22 Sep 2025 11:31:57 GMT
• Title: Tensor-Based Self-Calibration of Cameras via the TrifocalCalib Method
• Authors: Gregory Schroeder, Mohamed Sabry, Cristina Olaverri-Monreal,
• Abstract summary: Estimating camera intrinsic parameters without prior scene knowledge is a fundamental challenge in computer vision.<n>We present a set of equations based on the calibrated trifocal tensor, enabling projective camera self-calibration from minimal image data.
• Score: 0.24998872534482344
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Estimating camera intrinsic parameters without prior scene knowledge is a fundamental challenge in computer vision. This capability is particularly important for applications such as autonomous driving and vehicle platooning, where precalibrated setups are impractical and real-time adaptability is necessary. To advance the state-of-the-art, we present a set of equations based on the calibrated trifocal tensor, enabling projective camera self-calibration from minimal image data. Our method, termed TrifocalCalib, significantly improves accuracy and robustness compared to both recent learning-based and classical approaches. Unlike many existing techniques, our approach requires no calibration target, imposes no constraints on camera motion, and simultaneously estimates both focal length and principal point. Evaluations in both procedurally generated synthetic environments and structured dataset-based scenarios demonstrate the effectiveness of our approach. To support reproducibility, we make the code publicly available.

Related papers

• DST-Calib: A Dual-Path, Self-Supervised, Target-Free LiDAR-Camera Extrinsic Calibration Network [57.22935789233992]
  This article presents the first self-supervised LiDAR-camera extrinsic calibration network that operates in an online fashion.<n>The proposed method significantly outperforms existing approaches in terms of generalizability.
  arXiv  Detail & Related papers  (2026-01-03T13:57:01Z)
• CalibRefine: Deep Learning-Based Online Automatic Targetless LiDAR-Camera Calibration with Iterative and Attention-Driven Post-Refinement [7.736775961390864]
  CalibRefine is a fully automatic, targetless, and online calibration framework.<n>It directly processes raw LiDAR point clouds and camera images.<n>Our results show that robust object-level feature matching, combined with iterative refinement and self-supervised attention-based refinement, enables reliable sensor alignment.
  arXiv  Detail & Related papers  (2025-02-24T20:53:42Z)
• KRONC: Keypoint-based Robust Camera Optimization for 3D Car Reconstruction [58.04846444985808]
  This paper introduces KRONC, a novel approach aimed at inferring view poses by leveraging prior knowledge about the object to reconstruct and its representation through semantic keypoints. With a focus on vehicle scenes, KRONC is able to estimate the position of the views as a solution to a light optimization problem targeting the convergence of keypoints' back-projections to a singular point.
  arXiv  Detail & Related papers  (2024-09-09T08:08:05Z)
• VICAN: Very Efficient Calibration Algorithm for Large Camera Networks [49.17165360280794]
  We introduce a novel methodology that extends Pose Graph Optimization techniques. We consider the bipartite graph encompassing cameras, object poses evolving dynamically, and camera-object relative transformations at each time step. Our framework retains compatibility with traditional PGO solvers, but its efficacy benefits from a custom-tailored optimization scheme.
  arXiv  Detail & Related papers  (2024-03-25T17:47:03Z)
• P2O-Calib: Camera-LiDAR Calibration Using Point-Pair Spatial Occlusion Relationship [1.6921147361216515]
  We propose a novel target-less calibration approach based on the 2D-3D edge point extraction using the occlusion relationship in 3D space. Our method achieves low error and high robustness that can contribute to the practical applications relying on high-quality Camera-LiDAR calibration.
  arXiv  Detail & Related papers  (2023-11-04T14:32:55Z)
• PseudoCal: Towards Initialisation-Free Deep Learning-Based Camera-LiDAR Self-Calibration [5.263910852465186]
  Camera-LiDAR extrinsic calibration is a critical task for multi-sensor fusion in autonomous systems. Existing techniques often require manual intervention or specific environments, making them labour-intensive and error-prone. We present PseudoCal, a novel self-calibration method that overcomes these limitations by leveraging the pseudo-LiDAR concept and working directly in the 3D space.
  arXiv  Detail & Related papers  (2023-09-18T15:15:35Z)
• EasyHeC: Accurate and Automatic Hand-eye Calibration via Differentiable Rendering and Space Exploration [49.90228618894857]
  We introduce a new approach to hand-eye calibration called EasyHeC, which is markerless, white-box, and delivers superior accuracy and robustness. We propose to use two key technologies: differentiable rendering-based camera pose optimization and consistency-based joint space exploration. Our evaluation demonstrates superior performance in synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2023-05-02T03:49:54Z)
• Multi-task Learning for Camera Calibration [3.274290296343038]
  We present a unique method for predicting intrinsic (principal point offset and focal length) and extrinsic (baseline, pitch, and translation) properties from a pair of images. By reconstructing the 3D points using a camera model neural network and then using the loss in reconstruction to obtain the camera specifications, this innovative camera projection loss (CPL) method allows us that the desired parameters should be estimated.
  arXiv  Detail & Related papers  (2022-11-22T17:39:31Z)
• Self-Supervised Camera Self-Calibration from Video [34.35533943247917]
  We propose a learning algorithm to regress per-sequence calibration parameters using an efficient family of general camera models. Our procedure achieves self-calibration results with sub-pixel reprojection error, outperforming other learning-based methods.
  arXiv  Detail & Related papers  (2021-12-06T19:42:05Z)
• Self-Supervised Multi-Frame Monocular Scene Flow [61.588808225321735]
  We introduce a multi-frame monocular scene flow network based on self-supervised learning. We observe state-of-the-art accuracy among monocular scene flow methods based on self-supervised learning.
  arXiv  Detail & Related papers  (2021-05-05T17:49:55Z)
• Estimating Egocentric 3D Human Pose in Global Space [70.7272154474722]
  We present a new method for egocentric global 3D body pose estimation using a single-mounted fisheye camera. Our approach outperforms state-of-the-art methods both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2021-04-27T20:01:57Z)
• Infrastructure-based Multi-Camera Calibration using Radial Projections [117.22654577367246]
  Pattern-based calibration techniques can be used to calibrate the intrinsics of the cameras individually. Infrastucture-based calibration techniques are able to estimate the extrinsics using 3D maps pre-built via SLAM or Structure-from-Motion. We propose to fully calibrate a multi-camera system from scratch using an infrastructure-based approach.
  arXiv  Detail & Related papers  (2020-07-30T09:21:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.