単一の次善視点とロングテール多視点サンプリングによるワンショット視点計画の統合
Integrating One-Shot View Planning with a Single Next-Best View via Long-Tail Multiview Sampling
未知物体の3D再構成において、1回の次善視点(NBV)取得と多視点入力で学習したSCVPネットワークを組み合わせ、高品質かつ高効率な視点計画を実現した研究。
著者: Sicong Pan, Hao Hu, Hui Wei, Nils Dengler, Tobias Zaenker, Murad Dawood, Maren Bennewitz
分類: cs.RO
原文アブストラクト
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.
