Spatio-Temporal Road Scene Reconstruction using Superpixel MRF.
Year: 2018
Type: preprint
Abstract: Scene models construction based on image rendering is a hot topic in the computer vision community. In this paper, we propose a framework to construct road scene models based on 3D corridor structures. The construction of scene models consists of two successive stages: road detection and scene construction. The road detection is implemented via a new superpixel Markov random field (MRF) algorithm. The data fidelity term of the energy function is jointly computed using the superpixel features of color, texture and location. The smoothness term is defined by the interaction of spatio-temporally adjacent superpixels. The control points of road boundaries are generated with the constraint of vanishing point. Subsequently, the road scene models are constructed, where the foreground and background regions are modeled independently. Numerous applications are developed based on the proposed framework, e.g., traffic scenes simulation. The experiments and comparisons are conducted for both the road detection and scene construction stages, which prove the effectiveness of the proposed method.
Source: arXiv (Cornell University)
Authors: Yaochen Li, Yuehu Liu, Jihua Zhu, Shiqi Ma, Zhenning Niu, Rui Guo
Language: English
Topic: Advanced Vision and Imaging
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2901319369