Work

Reciprocal Visibility

Year: 2024

Type: preprint

Abstract: We propose a guidance strategy to optimize real-time synthetic aperture sampling for occlusion removal with drones by pre-scanned point-cloud data. Depth information can be used to compute visibility of points on the ground for individual drone positions in the air. Inspired by Helmholtz reciprocity, we introduce reciprocal visibility to determine the dual situation - the visibility of potential sampling position in the air from given points of interest on the ground. The resulting visibility map encodes which point on the ground is visible by which magnitude from any position in the air. Based on such a map, we demonstrate a first greedy sampling optimization.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2402.06991

Authors: Nathan, Rakesh John Amala Arokia, Strand, Sigrid, Shutin, Dmitriy, Bimber, Oliver

Institutions: Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR), Johannes Kepler University of Linz

Language: English

Topic: Advanced Vision and Imaging

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Deutsche Forschungsgemeinschaft, Austrian Science Fund, Bundesministerium für Bildung, Wissenschaft und Forschung

Awards: G105108620, G5108676691, G6159915257, Wide Synthetic Aperture Sampling for Motion Classification

OpenAlex ID: W4391800655

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