# Autonomous Implicit Indoor Scene Reconstruction with Frontier Exploration

arXiv: https://arxiv.org/abs/2404.10218 · HTML: https://mlsanity.com/paper/2404.10218/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 34 references that are in the map.
- First posted on arXiv 16 Apr 2024.

## Builds on (most cited first, 20 of 20)

- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction](https://mlsanity.com/paper/2106.10689/) (2021)
- [TensoRF: Tensorial Radiance Fields](https://mlsanity.com/paper/2203.09517/) (2022)
- [Volume Rendering of Neural Implicit Surfaces](https://mlsanity.com/paper/2106.12052/) (2021)
- [DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras](https://mlsanity.com/paper/2108.10869/) (2021)
- [Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI](https://mlsanity.com/paper/2109.08238/) (2021)
- [NICE-SLAM: Neural Implicit Scalable Encoding for SLAM](https://mlsanity.com/paper/2112.12130/) (2021)
- [Gibson Env: Real-World Perception for Embodied Agents](https://mlsanity.com/paper/1808.10654/) (2018)
- [MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction](https://mlsanity.com/paper/2206.00665/) (2022)
- [Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans](https://mlsanity.com/paper/2110.04994/) (2021)
- [ActiveNeRF: Learning where to See with Uncertainty Estimation](https://mlsanity.com/paper/2209.08546/) (2022)
- [NeurAR: Neural Uncertainty for Autonomous 3D Reconstruction with Implicit Neural Representations](https://mlsanity.com/paper/2207.10985/) (2022)
- [FUEL: Fast UAV Exploration using Incremental Frontier Structure and Hierarchical Planning](https://mlsanity.com/paper/2010.11561/) (2020)
- [An Efficient Sampling-based Method for Online Informative Path Planning in Unknown Environments](https://mlsanity.com/paper/1909.09548/) (2019)
- [FIESTA: Fast Incremental Euclidean Distance Fields for Online Motion Planning of Aerial Robots](https://mlsanity.com/paper/1903.02144/) (2019)
- [Active Neural Mapping](https://mlsanity.com/paper/2308.16246/) (2023)
- [Seal-3D: Interactive Pixel-Level Editing for Neural Radiance Fields](https://mlsanity.com/paper/2307.15131/) (2023)
- [Efficient View Path Planning for Autonomous Implicit Reconstruction](https://mlsanity.com/paper/2209.13159/) (2022)
- [History-aware Autonomous Exploration in Confined Environments using MAVs](https://mlsanity.com/paper/1803.10558/) (2018)
- [Asynchronous Collaborative Autoscanning with Mode Switching for Multi-Robot Scene Reconstruction](https://mlsanity.com/paper/2210.04413/) (2022)

## Built on by (most cited first, 1 of 1)

- [Multi-robot autonomous 3D reconstruction using Gaussian splatting with Semantic guidance](https://mlsanity.com/paper/2412.02249/) (2024)

## Abstract (arXiv)

Implicit neural representations have demonstrated significant promise for 3D scene reconstruction. Recent works have extended their applications to autonomous implicit reconstruction through the Next Best View (NBV) based method. However, the NBV method cannot guarantee complete scene coverage and often necessitates extensive viewpoint sampling, particularly in complex scenes. In the paper, we propose to 1) incorporate frontier-based exploration tasks for global coverage with implicit surface uncertainty-based reconstruction tasks to achieve high-quality reconstruction. and 2) introduce a method to achieve implicit surface uncertainty using color uncertainty, which reduces the time needed for view selection. Further with these two tasks, we propose an adaptive strategy for switching modes in view path planning, to reduce time and maintain superior reconstruction quality. Our method exhibits the highest reconstruction quality among all planning methods and superior planning efficiency in methods involving reconstruction tasks. We deploy our method on a UAV and the results show that our method can plan multi-task views and reconstruct a scene with high quality.