Vehicle Pose and Shape Estimation through Multiple Monocular Vision

TL;DR

This paper introduces a novel monocular vision-based method combining CNNs and optimization techniques to accurately estimate vehicle pose and shape from multiview images with minimal overlap, enhancing surveillance and transportation systems.

Contribution

The paper proposes a new approach integrating CNN-based keypoint detection with Cross Projection Optimization and Hierarchical Wireframe Constraint for improved 3D vehicle pose and shape estimation.

Findings

Outperforms existing monocular and stereo methods in accuracy

Effective in both simulated and real-world scenes

Provides a robust solution for vehicle localization and tracking

Abstract

In this paper, we present an accurate approach to estimate vehicles' pose and shape from off-board multiview images. The images are taken by monocular cameras and have small overlaps. We utilize state-of-the-art convolutional neural networks (CNNs) to extract vehicles' semantic keypoints and introduce a Cross Projection Optimization (CPO) method to estimate the 3D pose. During the iterative CPO process, an adaptive shape adjustment method named Hierarchical Wireframe Constraint (HWC) is implemented to estimate the shape. Our approach is evaluated under both simulated and real-world scenes for performance verification. It's shown that our algorithm outperforms other existing monocular and stereo methods for vehicles' pose and shape estimation. This approach provides a new and robust solution for off-board visual vehicle localization and tracking, which can be applied to massive surveillance camera networks for intelligent transportation.