Rozdziały

Stały URI dla kolekcjihttp://hdl.handle.net/11652/4775

Przeglądaj

Filtry

20232

Ustawienia

Autorsearch.filters.applied.operator.equals: search.filters.author.Belter, DominikTematsearch.filters.applied.operator.equals: search.filters.subject.robot perception

collection.search.results.head

Teraz wyświetlane 1 - 2 z 2
  • Pozycja
    NeRF-based RGB-D Images Generation in Robotics – Experimental Study
    (Wydawnictwo Politechniki Łódzkiej, 2023) Kulecki, Bartłomiej; Belter, Dominik
    Multiple learning-based algorithms in robotics require collecting RGB-D images of the scene from various viewpoints. These procedures are time-consuming, so many methods are trained using synthetic images. Recently, a Neural Radiance Fields (NeRF) model of the scene was proposed. Moreover, recent methods show that this model can be trained in minutes. This opens the possible applications in robotics for training the systems to reconstruct scenes, grasp objects or estimate their 3D poses using RGB-D images generated from a small number of input images. In this paper, we verify the quality of RGB-D images generated by the Instant Neural Graphics Primitives implementation of NeRF. We compare the obtained results from the Instant NeRF with the ground-truth RGB-D images obtained from the Kinect Azure and images generated from the point cloud model of the scene. The results show that the difference between generated RGB-D images and ground truth images is small, especially near the object.
  • Pozycja
    Mixing Synthetic and Real-world Datasets Strategy for Improved Generalization of the CNN
    (Wydawnictwo Politechniki Łódzkiej, 2023) Młodzikowski, Kamil; Belter, Dominik
    In this paper, we deal with the problem of supervised training neural networks with an insufficient number of real-world training examples. We propose a method that at the beginning trains the neural network using a relatively simple synthetic dataset. In the following epochs, we add more challenging and real-life images to the training dataset. We compare the proposed strategy with other methods of using artificial and real-world datasets for training the neural network. The obtained results show that the proposed strategy allows for obtaining the neural network with higher generalization capabilities than competitive methods.