  Existing view planning systems either adopt an iterative paradigm using
next-best views (NBV) or a one-shot pipeline relying on the set-covering
view-planning (SCVP) network. However, neither of these methods can
concurrently guarantee both high-quality and high-efficiency reconstruction of
3D unknown objects. To tackle this challenge, we introduce a crucial
hypothesis: with the availability of more information about the unknown object,
the prediction quality of the SCVP network improves. There are two ways to
provide extra information: (1) leveraging perception data obtained from NBVs,
and (2) training on an expanded dataset of multiview inputs. In this work, we
introduce a novel combined pipeline that incorporates a single NBV before
activating the proposed multiview-activated (MA-)SCVP network. The MA-SCVP is
trained on a multiview dataset generated by our long-tail sampling method,
which addresses the issue of unbalanced multiview inputs and enhances the
network performance. Extensive simulated experiments substantiate that our
system demonstrates a significant surface coverage increase and a substantial
45% reduction in movement cost compared to state-of-the-art systems. Real-world
experiments justify the capability of our system for generalization and
deployment.
