単眼車両姿勢推定のための中間表現の探求
Exploring intermediate representation for monocular vehicle pose estimation
単眼RGB画像から車両の3D姿勢を推定するため、補間直方体に基づく中間幾何表現を設計し、射影不変量に基づく損失関数でラベルなしデータも活用して学習する手法を提案した。
著者: Shichao Li, Zengqiang Yan, Hongyang Li, Kwang-Ting Cheng
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
We present a new learning-based framework to recover vehicle pose in SO(3) from a single RGB image. In contrast to previous works that map from local appearance to observation angles, we explore a progressive approach by extracting meaningful Intermediate Geometrical Representations (IGRs) to estimate egocentric vehicle orientation. This approach features a deep model that transforms perceived intensities to IGRs, which are mapped to a 3D representation encoding object orientation in the camera coordinate system. Core problems are what IGRs to use and how to learn them more effectively. We answer the former question by designing IGRs based on an interpolated cuboid that derives from primitive 3D annotation readily. The latter question motivates us to incorporate geometry knowledge with a new loss function based on a projective invariant. This loss function allows unlabeled data to be used in the training stage to improve representation learning. Without additional labels, our system outperforms previous monocular RGB-based methods for joint vehicle detection and pose estimation on the KITTI benchmark, achieving performance even comparable to stereo methods. Code and pre-trained models are available at this https URL.
