マルチステージCNNによる単眼3D車両位置・姿勢推定
Multi-Stage CNN-Based Monocular 3D Vehicle Localization and Orientation Estimation
単眼カメラ画像から鳥瞰図の高さマップと深層特徴を組み合わせ、車両の3Dバウンディングボックスと向きを推定するマルチブランチCNNモデルを提案し、合成データセットとKITTIで評価した。
著者: Ali Babolhavaeji, Mohammad Fanaei
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
This paper aims to design a 3D object detection model from 2D images taken by monocular cameras by combining the estimated bird's-eye view elevation map and the deep representation of object features. The proposed model has a pre-trained ResNet-50 network as its backend network and three more branches. The model first builds a bird's-eye view elevation map to estimate the depth of the object in the scene and by using that estimates the object's 3D bounding boxes. We have trained and evaluate it on two major datasets: a syntactic dataset and the KIITI dataset.