Thin-Plate Spline-based Adaptive 3D Surround View
Abstract
A “Bird's Eye View” (or Surround View) is a popular feature in modern cars and is particularly useful for parking and unparking purposes. This 3D reconstruction of the view uses multiple fisheye cameras and has traditionally been performed by a computer vision-based approach on a fixed mesh. This method is computationally expensive and does not always give ideal results for different types of surroundings. This paper discusses the design and implementation of a Thin-Plate Spline (TPS) algorithm for creating a 3D surround view of the vehicle with configurable vantage points and can be computed efficiently. Furthermore, it can choose from different meshes to adaptively generate a match with different surround-view environments on the fly. By surveying different environments and testing the implementation in a real car, we are able to demonstrate the practical feasibility of our solution.
The video demos of our algorithm have been uploaded to Youtube:
https://www.youtube.com/watch?v=-boYsUSA52c,
https://www.youtube.com/watch?v=NED435Uj12E.
https://www.youtube.com/watch?v=2sZTCIi4X2M&t=2s.
Our approach is competitive with the-state-of-the-art 3D reconstruction algorithms in computer vision while having a considerably lower runtime complexity.
BibTeX
@conference{Baek-2019-126188,author = {Iljoo Baek and Akshit Kanda and Tzu Chieh Tai and Anchan Saxena and Ragunathan Rajkumar},
title = {Thin-Plate Spline-based Adaptive 3D Surround View},
booktitle = {Proceedings of IEEE Intelligent Vehicles Symposium (IV '19)},
year = {2019},
month = {June},
pages = {586 - 593},
}