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Prediction-based active perception has shown the potential to improve thenavigation efficiency and safety of the robot by anticipating the uncertaintyin the unknown environment. The existing works for 3D shape prediction make animplicit assumption about the partial observations and therefore cannot be usedfor real-world planning and do not consider the control effort fornext-best-view planning. We present Pred-NBV, a realistic object shapereconstruction method consisting of PoinTr-C, an enhanced 3D prediction modeltrained on the ShapeNet dataset, and an information and control effort-basednext-best-view method to address these issues. Pred-NBV shows an improvement of25.46% in object coverage over the traditional methods in the AirSim simulator,and performs better shape completion than PoinTr, the state-of-the-art shapecompletion model, even on real data obtained from a Velodyne 3D LiDAR mountedon DJI M600 Pro.

Pred-NBV: Prediction-guided Next-Best-View for 3D Object Reconstruction