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Bio-Inspired Architecture for Deriving 3D Models from Video Sequences

  • Julius Schöning
  • Gunther Heidemann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10117)

Abstract

In an everyday context, automatic or interactive 3D reconstruction of objects from one or several videos is not yet possible. Humans, on the contrary, are capable of recognizing the 3D shape of objects even in complex video sequences. To enable machines for doing the same, we propose a bio-inspired processing architecture, which is motivated by the human visual system and converts video data into 3D representations. Similar to the hierarchy of the ventral stream, our process reduces the influence of the position information in the video sequences by object recognition and represents the object of interest as multiple pictorial representations. These multiple pictorial representations are showing 2D projections of the object of interest from different perspectives. Thus, a 3D point cloud can be obtained by multiple view geometry algorithms. In the course of a detailed presentation of this architecture, we additionally highlight existing analogies to the view-combination scheme. The potency of our architecture is shown by reconstructing a car out of two video sequences. In case the automatic processing cannot complete the task, the user is put in the loop to solve the problem interactively. This human-machine interaction facilitates a prototype implementation of the architecture, which can reconstruct 3D objects out of one or several videos. In conclusion, the strengths and limitations of our approach are discussed, followed by an outlook to future work to improve the architecture.

Keywords

Point Cloud Video Sequence Pictorial Representation Structure From Motion Inferior Temporal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Supplementary material

426014_1_En_5_MOESM1_ESM.zip (26.8 mb)
Supplementary material 1 (zip 27452 KB)

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Cognitive ScienceOsnabrück UniversityOsnabrückGermany

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