Unsupervised learning-based approach for detecting 3D edges in depth maps.

ABSTRACT: 3D edge features, which represent the boundaries between different objects or surfaces in a 3D scene, are crucial for many computer vision tasks, including object recognition, tracking, and segmentation. They also have numerous real-world applications in the field of robotics, such as vision-guided grasping and manipulation of objects. To extract these features in the noisy real-world depth data, reliable 3D edge detectors are indispensable. However, currently available 3D edge detection methods are either highly parameterized or require ground truth labelling, which makes them challenging to use for practical applications. To this extent, we present a new 3D edge detection approach using unsupervised classification. Our method learns features from depth maps at three different scales usin

SUBMITTER: Aggarwal A 

PROVIDER: S-EPMC10774289 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature