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The projection of a point cloud onto a 2D camera image is relevant in the case of various image analysis and enhancement tasks, e.g., (i) in multimodal image processing for data fusion, (ii) in robotic applications and in scene analysis, and (iii) for deep neural networks to generate real datasets with ground truth. The challenges of the current single-shot projection methods, such as simple state-of-the-art projection, conventional, polygon, and deep learning-based upsampling methods or closed source SDK functions of low-cost depth cameras, have been identified. We developed a new way to project point clouds onto a dense, accurate 2D raster image, called Triangle-Mesh-Rasterization-Projection (TMRP). The only gaps that the 2D image still contains with our method are valid gaps that result from the physical limits of the capturing cameras. Dense accuracy is achieved by simultaneously using the 2D neighborhood information (rx,ry) of the 3D coordinates in addition to the points P(X,Y,V). In this way, a fast triangulation interpolation can be performed. The interpolation weights are determined using sub-triangles. Compared to single-shot methods, our algorithm is able to solve the following challenges. This means that: (1) no false gaps or false neighborhoods are generated, (2) the density is XYZ independent, and (3) ambiguities are eliminated. Our TMRP method is also open source, freely available on GitHub, and can be applied to almost any sensor or modality. We also demonstrate the usefulness of our method with four use cases by using the KITTI-2012 dataset or sensors with different modalities. Our goal is to improve recognition tasks and processing optimization in the perception of transparent objects for robotic manufacturing processes.

The depth range that can be captured by structured-light 3D sensors is limited by the depth of field of the lenses which are used. Focus stacking is a common approach to extend the depth of field. However, focus variation drastically reduces the measurement speed of pattern projection-based sensors, hindering their use in high-speed applications such as in-line process control. Moreover, the lenses’ complexity is increased by electromechanical components, e.g., when applying electronically tunable lenses. In this contribution, we introduce chromatic focus stacking, an approach that allows for a very fast focus change by designing the axial chromatic aberration of an objective lens in a manner that the depth-of-field regions of selected wavelengths adjoin each other. In order to experimentally evaluate our concept, we determine the distance-dependent 3D modulation transfer function at a tilted edge and present the 3D measurement of a printed circuit board with comparatively high structures.

Recently, we have successfully tackled the challenge of measuring the 3D shape of uncooperative materials, i.e., materials with optical properties such as being glossy, transparent, absorbent, or translucent. By projecting sequential thermal fringes in the long-wave infrared (LWIR) combined with a stereo camera setup in the midwave infrared (MWIR), we were able to three-dimensionally record object shapes within one second. However, in many applications, e.g., for 100 % quality assurance, even shorter measurement times are required. To achieve camera frame rates higher than 125 fps at room temperature, Max Planck’s law of thermal emission teaches us a change in the camera spectral range from MWIR to LWIR. If irradiation and image acquisition have to run in parallel, the camera chips must therefore be protected against the radiation projected by the CO2 laser at a wavelength of 10.6 µm. Appropriate filters have been available only recently. In this contribution, we present our high-speed LWIR 3D sensor. The work includes a characterization of our setup regarding its measurement accuracy and speed. The results are compared to the performance of previous thermal 3D sensors. We show 3D measurement results of static objects as well as of a dynamic measurement situation of a transparent object. Furthermore, we demonstrate that our setup enables us to extend the measurability of material classes towards objects with high thermal conductivities.

The concept of Industry 4.0 brings the change of industry manufacturing patterns that become more efficient and more flexible. In response to this tendency, an efficient robot teaching approach without complex programming has become a popular research direction. Therefore, we propose an interactive finger-touch based robot teaching schema using a multimodal 3D image (color (RGB), thermal (T) and point cloud (3D)) processing. Here, the resulting heat trace touching the object surface will be analyzed on multimodal data, in order to precisely identify the true hand/object contact points. These identified contact points are used to calculate the robot path directly. To optimize the identification of the contact points we propose a calculation scheme using a number of anchor points which are first predicted by hand/object point cloud segmentation. Subsequently a probability density function is defined to calculate the prior probability distribution of true finger trace. The temperature in the neighborhood of each anchor point is then dynamically analyzed to calculate the likelihood. Experiments show that the trajectories estimated by our multimodal method have significantly better accuracy and smoothness than only by analyzing point cloud and static temperature distribution.