合成ドメインでの画像残差校正によるデータ駆動型6D姿勢追跡
Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains
合成画像と実画像の残差を校正するニューラルネットワークで6D物体姿勢をリアルタイム追跡し、合成データのみの学習でも実画像で高精度を達成した研究。
著者: Bowen Wen, Chaitanya Mitash, Kostas Bekris
分類: cs.CV, cs.RO
原文アブストラクト
Tracking the 6D pose of objects in video sequences is important for robot manipulation. This work presents se(3)-TrackNet, a data-driven optimization approach for long term, 6D pose tracking. It aims to identify the optimal relative pose given the current RGB-D observation and a synthetic image conditioned on the previous best estimate and the object's model. The key contribution in this context is a novel neural network architecture, which appropriately disentangles the feature encoding to help reduce domain shift, and an effective 3D orientation representation via Lie Algebra. Consequently, even when the network is trained solely with synthetic data can work effectively over real images. Comprehensive experiments over multiple benchmarks show se(3)-TrackNet achieves consistently robust estimates and outperforms alternatives, even though they have been trained with real images. The approach runs in real time at 90.9Hz. Code, data and supplementary video for this project are available at https://github.com/wenbowen123/iros20-6d-pose-tracking
