Abstract
Abstract. In the last years, vision-based systems have flourished at an unprecedented pace, fuelled by developments in hardware components (higher resolution and higher sensitivity imaging sensors, smaller and smarter micro controllers, just to name a few), as well as in software or processing techniques, with AI (Artificial Intelligence) leading to a landmark revolution. Several disciplines have fostered and benefited from these advances, but, unfortunately, not always in a coordinated and cooperative way.When it comes to image-based sensing techniques, photogrammetry, computer vision and robotic vision have many contact points and overlapping areas. Yet, as for people of different cultures and languages, communicating among the three different communities can be very harsh and disorienting - especially for beginners and non-specialists.Driven by a strong educational and inclusive ambition, the LightCam project is funded by the ISPRS Education and Capacity Building Initiatives 2020 (ECB). The project’s ambition is to act as an interpreter and ease the dialog among the three actors, i.e. photogrammetry, computer vision and robotics. Two intermediation tools will be developed to serve this aim: (i) a dictionary of concepts, terminology and algorithms, in the form of a knowledge base website, and (ii) a code repository, where pieces of code for the conversion between different formulations implemented in available software solutions will be shared.
Highlights
1.1 Background and motivationsWe perceive the world through our senses, where vision is the one we rely on the most
It was not long after its invention that photography was introduced to map cities (Jensen, 2007) or survey buildings (Albertz, 2002), applications that represented the birth of photogrammetry
The book by (Förstner & Wrobel, 2016), entitled “Photogrammetric Computer Vision”, addresses concepts and methods developed in the computer vision community with a focus on the main application areas of photogrammetry, mapping and image-based metrology
Summary
We perceive the world through our senses, where vision is the one we rely on the most. Paintings and images were the preferred form of representation, documentation and communication before the invention of photography, which marked a milestone in art and in engineering It was not long after its invention that photography was introduced to map cities (Jensen, 2007) or survey buildings (Albertz, 2002), applications that represented the birth of photogrammetry. Computer vision designs computational models able to reproduce the human visual system to support autonomous systems (Huang, 1996) It investigates theory and methods for the automatic extraction, analysis and understanding of information from single images or sequences. The book by (Förstner & Wrobel, 2016), entitled “Photogrammetric Computer Vision”, addresses concepts and methods developed in the computer vision community with a focus on the main application areas of photogrammetry, mapping and image-based metrology. Software solutions and algorithms coming from different communities, sharing a common theoretical basis, may entail different formulations and implementations (Fig. 1; Drap & Lefèvre, 2016; Borlin et al, 2019)
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