NeRF-based RGB-D Images Generation in Robotics – Experimental Study
Ładowanie...
Data
2023
Autorzy
Tytuł czasopisma
ISSN czasopisma
Tytuł tomu
Wydawca
Wydawnictwo Politechniki Łódzkiej
Lodz University of Technology Press
Lodz University of Technology Press
Abstrakt
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.
Opis
Słowa kluczowe
robot perception, image synthesis, percepcja robotów, synteza obrazu
Cytowanie
Kulecki B., Belter D., NeRF-based RGB-D Images Generation in Robotics – Experimental Study. W: Progress in Polish Artificial Intelligence Research 4, Wojciechowski A. (Ed.), Lipiński P. (Ed.)., Seria: Monografie Politechniki Łódzkiej Nr. 2437, Wydawnictwo Politechniki Łódzkiej, Łódź 2023, s. 443-448, ISBN 978-83-66741-92-8, doi: 10.34658/9788366741928.69.