CALIBRATION OF 3D KINEMATIC SYSTEMS USING 2D CALIBRATION PLATE

3D kinematic systems based on the images acquired by cameras are one of the most popular tools for a human motion analyses. Prior to the actual reconstruction a camera calibration procedure is needed. Originally 3D calibration cages were utilized for that purpose, but nowadays a vast majority of commercial systems rely on the wand calibration. When the highest degree of accuracy is requested, than using 3D calibration cage is often recommended over the wand calibration. On the other hand, from a user point of view a wand calibration is generally regarded as the most user friendly. A substantial ‘intermediate’ solution would be using 2D calibration plate. Interestingly, there could be hardly found any trace that commercial 3D kinematic systems ever relied on 2D calibration plate. The purpose of this study was to investigate quantitative and qualitative aspects of calibrating the 3D kinematic system using 2D calibration plate

Towards Keypoint Guided Self-Supervised Depth Estimation

This paper proposes to use keypoints as a self-supervision clue for learning depth map estimation from a collection of input images. As ground truth depth from real images is difficult to obtain, there are many unsupervised and self-supervised approaches to depth estimation that have been proposed. Most of these unsupervised approaches use depth map and ego-motion estimations to reproject the pixels from the current image into the adjacent image from the image collection. Depth and ego-motion estimations are evaluated based on pixel intensity differences between the correspondent original and reprojected pixels. Instead of reprojecting the individual pixels, we propose to first select image keypoints in both images and then reproject and compare the correspondent keypoints of the two images. The keypoints should describe the distinctive image features well. By learning a deep model with and without the keypoint extraction technique, we show that using the keypoints improve the depth estimation learning. We also propose some future directions for keypoint-guided learning of structure-from-motion problems

A Practical Way to Initialize Camera Parameters using the Absolute Conic

A very common and effective approach for 3D reconstruction is a camera based system where 3D information is extracted from images. Different systems involve different camera calibration methods/tools. Characteristically for many systems is to calibrate the cameras using a single wand of known length. As integral part of the calibration procedure, initial camera parameters are commonly computed by putting and imaging two or three orthogonal wands inside the working volume. This is usually followed by the second step: sweeping the working volume with a single wand of known length. This paper presents two alternative ways of initializing camera parameters using essentially the same calibration tools (orthogonal wands), however by sweeping the volume with an orthogonal pair or triad of wands instead of a single one. The proposed methods exploit the orthogonality of the used wands and familiar linear constraints to calculate the image of the so-called absolute conic (IAC). Extracted internal parameters values from IAC are closer to the refined ones, assuring faster and safer convergence. Even without refinement, sometimes not necessary, reconstruction results using our initial sets are better than using commonly obtained initial values. Besides, the entire calibration procedure is shortened since the usual two calibration steps become one

Camera Parameter Initialization for 3D Kinematic Systems

3D information of the scene can be extracted from images acquired by cameras. Before the actual reconstruction camera calibration has to be done. Reconstruction accuracy is highly dictated by the calibration. Two typical demands, which are not easily simultaneously satisfied, are: calibration has to be done in fast and convenient manner and yet assure high degree of reconstruction accuracy. Computational part of calibration usually includes camera parameters initialization and refinement based on initial set of values. The goodness of initial set greatly affects refinement procedure in terms of convergence speed and ultimately reconstruction accuracy. This work proposes new calibration method for 3D kinematic systems. It shortens commonly used calibration procedure, gives better initial parameter values for refinement procedure which in turn is supposed to assure faster and safer convergence of iterative minimization algorithm. Additionally, it will be shown that even without parameter refinement proposed method gives more accurate 3D reconstruction output. 1