Stereo camera system calibration: the need of two sets of parameters
- URL: http://arxiv.org/abs/2101.05725v1
- Date: Thu, 14 Jan 2021 17:03:17 GMT
- Title: Stereo camera system calibration: the need of two sets of parameters
- Authors: Riccardo Beschi, Xiao Feng, Stefania Melillo, Leonardo Parisi, Lorena
Postiglione
- Abstract summary: The reconstruction of a scene via a stereo-camera system is a two-steps process.
At first images from different cameras are matched to identify the set of point-to-point correspondences that then will actually be reconstructed in the real world.
We propose to calibrate the system twice to estimate two different sets of parameters.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The reconstruction of a scene via a stereo-camera system is a two-steps
process, where at first images from different cameras are matched to identify
the set of point-to-point correspondences that then will actually be
reconstructed in the three dimensional real world. The performance of the
system strongly relies of the calibration procedure, which has to be carefully
designed to guarantee optimal results. We implemented three different
calibration methods and we compared their performance over 19 datasets. We
present the experimental evidence that, due to the image noise, a single set of
parameters is not sufficient to achieve high accuracy in the identification of
the correspondences and in the 3D reconstruction at the same time. We propose
to calibrate the system twice to estimate two different sets of parameters: the
one obtained by minimizing the reprojection error that will be used when
dealing with quantities defined in the 2D space of the cameras, and the one
obtained by minimizing the reconstruction error that will be used when dealing
with quantities defined in the real 3D world.
Related papers
- Neural Real-Time Recalibration for Infrared Multi-Camera Systems [2.249916681499244]
There are no learning-free or neural techniques for real-time recalibration of infrared multi-camera systems.
We propose a neural network-based method capable of dynamic real-time calibration.
arXiv Detail & Related papers (2024-10-18T14:37:37Z) - Shakes on a Plane: Unsupervised Depth Estimation from Unstabilized
Photography [54.36608424943729]
We show that in a ''long-burst'', forty-two 12-megapixel RAW frames captured in a two-second sequence, there is enough parallax information from natural hand tremor alone to recover high-quality scene depth.
We devise a test-time optimization approach that fits a neural RGB-D representation to long-burst data and simultaneously estimates scene depth and camera motion.
arXiv Detail & Related papers (2022-12-22T18:54:34Z) - Uncertainty-Aware Camera Pose Estimation from Points and Lines [101.03675842534415]
Perspective-n-Point-and-Line (Pn$PL) aims at fast, accurate and robust camera localizations with respect to a 3D model from 2D-3D feature coordinates.
arXiv Detail & Related papers (2021-07-08T15:19:36Z) - Calibrated and Partially Calibrated Semi-Generalized Homographies [65.29477277713205]
We propose the first minimal solutions for estimating the semi-generalized homography given a perspective and a generalized camera.
The proposed solvers are stable and efficient as demonstrated by a number of synthetic and real-world experiments.
arXiv Detail & Related papers (2021-03-11T08:56:24Z) - PLUME: Efficient 3D Object Detection from Stereo Images [95.31278688164646]
Existing methods tackle the problem in two steps: first depth estimation is performed, a pseudo LiDAR point cloud representation is computed from the depth estimates, and then object detection is performed in 3D space.
We propose a model that unifies these two tasks in the same metric space.
Our approach achieves state-of-the-art performance on the challenging KITTI benchmark, with significantly reduced inference time compared with existing methods.
arXiv Detail & Related papers (2021-01-17T05:11:38Z) - Automated Calibration of Mobile Cameras for 3D Reconstruction of
Mechanical Pipes [0.0]
This manuscript provides a new framework for calibration of optical instruments, in particular mobile cameras, using large-scale circular black and white target fields.
New methods were introduced for (i) matching targets between images; (ii) adjusting the systematic error of target centers; and (iii) iteratively improving the calibration solution through a free-network self-calibrating bundle adjustment.
arXiv Detail & Related papers (2020-12-04T23:41:25Z) - Lightweight Multi-View 3D Pose Estimation through Camera-Disentangled
Representation [57.11299763566534]
We present a solution to recover 3D pose from multi-view images captured with spatially calibrated cameras.
We exploit 3D geometry to fuse input images into a unified latent representation of pose, which is disentangled from camera view-points.
Our architecture then conditions the learned representation on camera projection operators to produce accurate per-view 2d detections.
arXiv Detail & Related papers (2020-04-05T12:52:29Z) - Multi-View Photometric Stereo: A Robust Solution and Benchmark Dataset
for Spatially Varying Isotropic Materials [65.95928593628128]
We present a method to capture both 3D shape and spatially varying reflectance with a multi-view photometric stereo technique.
Our algorithm is suitable for perspective cameras and nearby point light sources.
arXiv Detail & Related papers (2020-01-18T12:26:22Z) - Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless
Approach [32.15405927679048]
We propose a targetless and structureless camera-DAR calibration method.
Our method combines a closed-form solution with a structureless bundle where the coarse-to-fine approach does not require an initial adjustment on the temporal parameters.
We demonstrate the accuracy and robustness of the proposed method through both simulation and real data experiments.
arXiv Detail & Related papers (2020-01-17T07:25:59Z) - A Two-step Calibration Method for Unfocused Light Field Camera Based on
Projection Model Analysis [8.959346460518226]
The proposed method is able to reuse traditional camera calibration methods for the direction parameter set.
The accuracy and robustness of the proposed method outperforms its counterparts under various benchmark criteria.
arXiv Detail & Related papers (2020-01-11T10:37:56Z)
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.