Sim-to-Real Dataset of Industrial Metal Objects

We present a diverse dataset of industrial metal objects with unique characteristics such as symmetry, texturelessness, and high reflectiveness. These features introduce challenging conditions that are not captured in existing datasets. Our dataset comprises both real-world and synthetic multi-view RGB images with 6D object pose labels. Real-world data were obtained by recording multi-view images of scenes with varying object shapes, materials, carriers, compositions, and lighting conditions. This resulted in over 30,000 real-world images. We introduce a new public tool that enables the quick annotation of 6D object pose labels in multi-view images. This tool was used to provide 6D object pose labels for all real-world images. Synthetic data were generated by carefully simulating real-world conditions and varying them in a controlled and realistic way. This resulted in over 500,000 synthetic images. The close correspondence between synthetic and real-world data and controlled variations will facilitate sim-to-real research. Our focus on industrial conditions and objects will facilitate research on computer vision tasks, such as 6D object pose estimation, which are relevant for many industrial applications, such as machine tending. The dataset and accompanying resources are available on the project website

CenDerNet : center and curvature representations for render-and-compare 6D pose estimation

We introduce CenDerNet, a framework for 6D pose estimation from multi-view images based on center and curvature representations. Finding precise poses for reflective, textureless objects is a key challenge for industrial robotics. Our approach consists of three stages: First, a fully convolutional neural network predicts center and curvature heatmaps for each view; Second, center heatmaps are used to detect object instances and find their 3D centers; Third, 6D object poses are estimated using 3D centers and curvature heatmaps. By jointly optimizing poses across views using a render-and-compare approach, our method naturally handles occlusions and object symmetries. We show that CenDerNet outperforms previous methods on two industry-relevant datasets: DIMO and T-LESS

In the mood : how sexual desire predicts and is predicted by romantic partners’ mood

The association between mood and sexual desire has been the object of significant scientific and public interest. How mood shapes and is shaped by sexual desire is typically studied within one and the same individual, yet sexual desire is often experienced in the context of a romantic relationship. To obtain a more complete picture of the relation between mood and sexual desire, we examined the temporal interplay between mood and sexual desire both within and between partners in a romantic relationship. Using data from an experience sampling study involving both partners of mixed-gender romantic couples (N = 188; Mage = 26.34, SDage = 5.33), we investigated how each partner's mood (in terms of positive and negative affect) predicted their own sexual desire as well as that of their partner and vice versa. Results of both concurrent and temporal analyses confirmed bidirectional associations between mood and sexual desire both within and between partners, such that (1) both a person's own and their partner's positive mood predicted an increase in sexual desire, and a person's own and their partner's negative mood predicted a decrease in sexual desire. In addition, (2) both a person's own and their partner's sexual desire predicted an increase in positive mood, and a person's own and their partner's sexual desire predicted a decrease in negative mood. Only a few gender differences were found. The results underscore how sexual desire can predict and be predicted by both romantic partners' mood, highlighting the need for interactional models of sexual desire