Marcel Brückner

Intrinsic and extrinsic active self-calibration of multi-camera systems

We present a method for active self-calibration of multi-camera systems consisting of pan-tilt zoom cameras. The main focus of this work is on extrinsic self-calibration using active camera control. Our novel probabilistic approach avoids multi-image point correspondences as far as possible. This allows an implicit treatment of ambiguities. The relative poses are optimized by actively rotating and zooming each camera pair in a way that significantly simplifies the problem of extracting correct point correspondences. In a final step we calibrate the entire system using a minimal number of relative poses. The selection of relative poses is based on their uncertainty. We exploit active camera control to estimate consistent translation scales for triplets of cameras. This allows us to estimate missing relative poses in the camera triplets. In addition to this active extrinsic self-calibration we present an extended method for the rotational intrinsic self-calibration of a camera that exploits the rotation knowledge provided by the camera’s pan-tilt unit to robustly estimate the intrinsic camera parameters for different zoom steps as well as the rotation between pan-tilt unit and camera. Quantitative experiments on real data demonstrate the robustness and high accuracy of our approach. We achieve a median reprojection error of

An Efficient Shortest Triangle Paths Algorithm Applied to Multi-camera Self-calibration

0.95

Temporal Estimation of the 3d Guide-Wire Position Using 2d X-ray Images

0.95 pixel.

Exploiting the Manhattan-world assumption for extrinsic self-calibration of multi-modal sensor networks

We present a method for actively calibrating a multicamera system consisting of pan-tilt zoom cameras. After a coarse initial calibration, we determine the probability of each relative pose using a probability distribution based on the camera images. The relative poses are optimized by rotating and zooming each camera pair in a way that significantly simplifies the problem of extracting correct point correspondences. In a final step we use active camera control, the optimized relative poses, and their probabilities to calibrate the complete multi-camera system with a minimal number of relative poses. During this process we estimate the translation scales in a camera triangle using only two of the three relative poses and no point correspondences. Quantitative experiments on real data outline the robustness and accuracy of our approach.

Experimental Evaluation of Relative Pose Estimation Algorithms.

We propose a novel minimum uncertainty approach to relative pose selection for multi-camera self-calibration. We show how this discrete global optimization problem can be expressed as a shortest triangle paths problem. For the latter, we present an efficient algorithm and prove its correctness. It has several advantages compared to a similar approach of Vergés-Llahí, Moldovan and Wada. In quantitative experiments on publically available data, we show that our relative pose selection method provides improvements compared to naive, random and greedy selection, without and with subsequent bundle adjustment.

SELF-CALIBRATION OF CAMERA NETWORKS: ACTIVE AND PASSIVE METHODS

Detecting image pairs with a common field of view is an important prerequisite for many computer vision tasks. Typically, common local features are used as a criterion for identifying such image pairs. This approach, however, requires a reliable method for matching features, which is generally a very difficult problem, especially in situations with a wide baseline or ambiguities in the scene. We propose two new approaches for the common field of view problem. The first one is still based on feature matching. Instead of requiring a very low false positive rate for the feature matching, however, geometric constraints are used to assess matches which may contain many false positives. The second approach completely avoids hard matching of features by evaluating the entropy of correspondence probabilities. We perform quantitative experiments on three different hand labeled scenes with varying difficulty. In moderately difficult situations with a medium baseline and few ambiguities in the scene, our proposed methods give similarly good results to the classical matching based method. On the most challenging scene having a wide baseline and many ambiguities, the performance of the classical method deteriorates, while ours are much less affected and still produce good results. Hence, our methods show the best overall performance in a combined evaluation.

Using Common Field of View Detection for Multi Camera Calibration.

We present a method for realtime online 3d reconstruction of a guide-wire or catheter using 2d X-ray images, which do not have to be recorded from different viewpoints. No special catheters or sensors are needed. Given a 3d patient data set and the projection parameters, we use recursive probability density propagation to estimate a probability distribution of the current positions of guide-wire parts. Based on this distribution, we extract the optimal guide-wire position using regularization techniques. We describe the guide-wire position by a uniform cubic B-spline. Experiments on simulated and phantom data demonstrate the high accuracy and robustness of our approach.

Extrinsic Self-Calibration of Time-of-Flight Cameras using a Combination of 3D and Intensity Descriptors

Many new applications are enabled by combining a multi-camera system with a Time-of-Flight (ToF) camera, which is able to simultaneously record intensity and depth images. Classical approaches for self-calibration of a multi-camera system fail to calibrate such a system due to the very different image modalities. In addition, the typical environments of multi-camera systems are man-made and consist primary of only low textured objects. However, at the same time they satisfy the Manhattan-world assumption. We formulate the multi-modal sensor network calibration as a Maximum a Posteriori (MAP) problem and solve it by minimizing the corresponding energy function. First we estimate two separate 3D reconstructions of the environment: one using the pan-tilt unit mounted ToF camera and one using the multi-camera system. We exploit the Manhattan-world assumption and estimate multiple initial calibration hypotheses by registering the three dominant orientations of planes. These hypotheses are used as prior knowledge of a subsequent MAP estimation aiming to align edges that are parallel to these dominant directions. To our knowledge, this is the first self-calibration approach that is able to calibrate a ToF camera with a multi-camera system. Quantitative experiments on real data demonstrate the high accuracy of our approach.