# Efficient View Path Planning for Autonomous Implicit Reconstruction

arXiv: https://arxiv.org/abs/2209.13159 · HTML: https://mlsanity.com/paper/2209.13159/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2022.

## Built on by (most cited first, 11 of 11)

- [Active Neural Mapping](https://mlsanity.com/paper/2308.16246/) (2023)
- [Improving NeRF Quality by Progressive Camera Placement for Unrestricted Navigation in Complex Environments](https://mlsanity.com/paper/2309.00014/) (2023)
- [Seal-3D: Interactive Pixel-Level Editing for Neural Radiance Fields](https://mlsanity.com/paper/2307.15131/) (2023)
- [NeRFs in Robotics: A Survey](https://mlsanity.com/paper/2405.01333/) (2024)
- [AutoNeRF: Training Implicit Scene Representations with Autonomous Agents](https://mlsanity.com/paper/2304.11241/) (2023)
- [SealD-NeRF: Interactive Pixel-Level Editing for Dynamic Scenes by Neural Radiance Fields](https://mlsanity.com/paper/2402.13510/) (2024)
- [REF2-NeRF: Reflection and Refraction aware Neural Radiance Field](https://mlsanity.com/paper/2311.17116/) (2023)
- [Autonomous Implicit Indoor Scene Reconstruction with Frontier Exploration](https://mlsanity.com/paper/2404.10218/) (2024)
- [Multi-robot autonomous 3D reconstruction using Gaussian splatting with Semantic guidance](https://mlsanity.com/paper/2412.02249/) (2024)
- [Hestia: Voxel-Face-Aware Hierarchical Next-Best-View Acquisition for Efficient 3D Reconstruction](https://mlsanity.com/paper/2508.01014/) (2025)
- [VCURF: Virtual Camera-based Uncertainty of Radiance Fields](https://mlsanity.com/paper/2610.04076/) (2026)

## Abstract (arXiv)

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.