Pred-NBV: 予測誘導型次善視点による3次元物体再構成
Pred-NBV: Prediction-guided Next-Best-View for 3D Object Reconstruction
3D形状予測モデルPoinTr-Cと情報・制御コストに基づく次善視点計画を組み合わせ、物体形状を効率的に再構成する手法を提案。AirSimと実機LiDARで従来手法より高いカバレッジと形状補完性能を示した。
著者: Harnaik Dhami, Vishnu D. Sharma, Pratap Tokekar
分類: cs.RO
原文アブストラクト
Prediction-based active perception has shown the potential to improve the navigation efficiency and safety of the robot by anticipating the uncertainty in the unknown environment. The existing works for 3D shape prediction make an implicit assumption about the partial observations and therefore cannot be used for real-world planning and do not consider the control effort for next-best-view planning. We present Pred-NBV, a realistic object shape reconstruction method consisting of PoinTr-C, an enhanced 3D prediction model trained on the ShapeNet dataset, and an information and control effort-based next-best-view method to address these issues. Pred-NBV shows an improvement of 25.46% in object coverage over the traditional methods in the AirSim simulator, and performs better shape completion than PoinTr, the state-of-the-art shape completion model, even on real data obtained from a Velodyne 3D LiDAR mounted on DJI M600 Pro.
