Institute of Cognitive Science

Research Group Computer Vision


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

3DMA16@ACCV 2016 - Supplementary Material

Abstract In an everyday context, automatic or interactive 3D reconstruction of objects from one or several videos is not yet possible. Humans, however, 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 structure from motion 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 the case of processing modules cannot complete the task automatically, the user is put in the loop with the aim of solving 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 a forecast of further work to improve and further automatized the architecture.

Demonstration Video:

Demonstration video showing our new Bio-Inspired Architecture for Deriving 3D Models from Video Sequences

References:

[1] J. Schöning & G. Heidemann.
Bio-Inspired Architecture for Deriving 3D Models from Video Sequences.
Computer Vision -- ACCV 2016 Workshop, pages: 62-76, ISBN: 978-3-319-54427-4, 2016. Springer Nature.
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