Leveraging Foundation Models To learn the shape of semi-fluid deformable objects

One of the difficulties imposed on the manipulation of deformable objects is their characterization and the detection of representative keypoints for the purpose of manipulation. A keen interest was manifested by researchers in the last decade to characterize and manipulate deformable objects of non-fluid nature, such as clothes and ropes. Even though several propositions were made in the regard of object characterization, however researchers were always confronted with the need of pixel-level information of the object through images to extract relevant information. This usually is accomplished by means of segmentation networks trained on manually labeled data for this purpose. In this paper, we address the subject of characterizing weld pool to define stable features that serve as information for further motion control objectives. We achieve this by employing different pipelines. The first one consists of characterizing fluid deformable objects through the use of a generative model that is trained using a teacher-student framework. And in the second one we leverage foundation models by using them as teachers to characterize the object in the image, without the need of any pre-training and any dataset. The performance of knowledge distillation from foundation models into a smaller generative model shows prominent results in the characterization of deformable objects. The student network was capable of learning to retrieve the keypoitns of the object with an error of 13.4 pixels. And the teacher was evaluated based on its capacities to retrieve pixel level information represented by the object mask, with a mean Intersection Over Union (mIoU) of 75.26%.

Toward Efficient Visual Gyroscopes: Spherical Moments, Harmonics Filtering, and Masking Techniques for Spherical Camera Applications

Unlike a traditional gyroscope, a visual gyroscope estimates camera rotation through images. The integration of omnidirectional cameras, offering a larger field of view compared to traditional RGB cameras, has proven to yield more accurate and robust results. However, challenges arise in situations that lack features, have substantial noise causing significant errors, and where certain features in the images lack sufficient strength, leading to less precise prediction results. Here, we address these challenges by introducing a novel visual gyroscope, which combines an analytical method with a neural network approach to provide a more efficient and accurate rotation estimation from spherical images. The presented method relies on three key contributions: an adapted analytical approach to compute the spherical moments coefficients, introduction of masks for better global feature representation, and the use of a multilayer perceptron to adaptively choose the best combination of masks and filters. Experimental results demonstrate superior performance of the proposed approach in terms of accuracy. The paper emphasizes the advantages of integrating machine learning to optimize analytical solutions, discusses limitations, and suggests directions for future research.