RESUMO
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.
RESUMO
Estimating depth from images is a common technique in 3D perception. However, dealing with non-Lambertian materials, e.g., transparent or specular, is still nowadays an open challenge. However, to overcome this challenge with deep stereo matching networks or monocular depth estimation, data sets with non-Lambertian objects are mandatory. Currently, only few real-world data sets are available. This is due to the high effort and time-consuming process of generating these data sets with ground truth. Currently, transparent objects must be prepared, e.g., painted or powdered, or an opaque twin of the non-Lambertian object is needed. This makes data acquisition very time consuming and elaborate. We present a new measurement principle for how to generate a real data set of transparent and specular surfaces without object preparation techniques, which greatly reduces the effort and time required for data collection. For this purpose, we use a thermal 3D sensor as a reference system, which allows the 3D detection of transparent and reflective surfaces without object preparation. In addition, we publish the first-ever real stereo data set, called TranSpec3D, where ground truth disparities without object preparation were generated using this measurement principle. The data set contains 110 objects and consists of 148 scenes, each taken in different lighting environments, which increases the size of the data set and creates different reflections on the surface. We also show the advantages and disadvantages of our measurement principle and data set compared to the Booster data set (generated with object preparation), as well as the current limitations of our novel method.