高密度から低密度への深層ドメイン適応による堅牢な3D物体認識
Towards Robust 3D Object Recognition with Dense-to-Sparse Deep Domain Adaptation
高密度点群のみで学習しながら、教師なしドメイン適応により低密度点群でも高精度に3D物体認識できる手法を提案した論文。
著者: Prajval Kumar Murali, Cong Wang, Ravinder Dahiya, Mohsen Kaboli
分類: cs.RO, cs.CV
原文アブストラクト
Three-dimensional (3D) object recognition is crucial for intelligent autonomous agents such as autonomous vehicles and robots alike to operate effectively in unstructured environments. Most state-of-art approaches rely on relatively dense point clouds and performance drops significantly for sparse point clouds. Unsupervised domain adaption allows to minimise the discrepancy between dense and sparse point clouds with minimal unlabelled sparse point clouds, thereby saving additional sparse data collection, annotation and retraining costs. In this work, we propose a novel method for point cloud based object recognition with competitive performance with state-of-art methods on dense and sparse point clouds while being trained only with dense point clouds.
