Direct Object Reconstruction on RGB-D Images in Cluttered Environment скачать в хорошем качестве

Direct Object Reconstruction on RGB-D Images in Cluttered Environment

Robots have limited perception capabilities when observing the scene from a single viewpoint. Some objects on the scene might be partially occluded and their 3D shape is not fully available to the robot. Existing methods obtain object models through a series of observations using RGB-D sensors or the robot is trained to operate in the presence of occlusions. In this paper, we directly address object reconstruction in the presence of occlusions. We propose an image generation approach using a neural network architecture to remove occluding objects and other objects from RGB-D images and reconstruct the occluded object that the robot is interested in. The proposed method utilizes a cascade of neural networks trained to progressively remove occlusions and reconstruct the RGB-D images of the scene. [1] Mikołaj Zieliński, Bartłomiej Kulecki, Dominik Belter, Direct Object Reconstruction on RGB-D Images in Cluttered Environment, European Conference on Mobile Robots, 2023 http://sonata.put.poznan.pl