Efficient View Path Planning for Autonomous Implicit Reconstruction

TL;DR

This paper introduces an efficient view path planning method for autonomous 3D scene reconstruction that combines neural implicit functions with volumetric representations, significantly improving reconstruction quality and planning speed.

Contribution

It proposes a novel approach that leverages neural networks for information gain estimation and combines implicit and volumetric representations for efficient view planning.

Findings

Significant improvements in reconstruction quality.

Enhanced planning efficiency over existing methods.

Successful deployment on a real UAV.

Abstract

Implicit neural representations have shown promising potential for the 3D scene reconstruction. Recent work applies it to autonomous 3D reconstruction by learning information gain for view path planning. Effective as it is, the computation of the information gain is expensive, and compared with that using volumetric representations, collision checking using the implicit representation for a 3D point is much slower. In the paper, we propose to 1) leverage a neural network as an implicit function approximator for the information gain field and 2) combine the implicit fine-grained representation with coarse volumetric representations to improve efficiency. Further with the improved efficiency, we propose a novel informative path planning based on a graph-based planner. Our method demonstrates significant improvements in the reconstruction quality and planning efficiency compared with autonomous reconstructions with implicit and explicit representations. We deploy the method on a real UAV and the results show that our method can plan informative views and reconstruct a scene with high quality.