# SC-Explorer: Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and Planning

arXiv: https://arxiv.org/abs/2208.08307 · HTML: https://mlsanity.com/paper/2208.08307/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 63 references that are in the map.
- First posted on arXiv 17 Aug 2022.

## Builds on (most cited first, 30 of 30)

- [Deep Reinforcement Learning that Matters](https://mlsanity.com/paper/1709.06560/) (2017)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [Semantic Scene Completion from a Single Depth Image](https://mlsanity.com/paper/1611.08974/) (2016)
- [Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning](https://mlsanity.com/paper/1611.03631/) (2016)
- [SemanticFusion: Dense 3D Semantic Mapping with Convolutional Neural Networks](https://mlsanity.com/paper/1609.05130/) (2016)
- [3D Sketch-aware Semantic Scene Completion via Semi-supervised Structure Prior](https://mlsanity.com/paper/2003.14052/) (2020)
- [The Limits and Potentials of Deep Learning for Robotics](https://mlsanity.com/paper/1804.06557/) (2018)
- [Fusion++: Volumetric Object-Level SLAM](https://mlsanity.com/paper/1808.08378/) (2018)
- [3D Semantic Scene Completion: a Survey](https://mlsanity.com/paper/2103.07466/) (2021)
- [DI-Fusion: Online Implicit 3D Reconstruction with Deep Priors](https://mlsanity.com/paper/2012.05551/) (2020)
- [RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion](https://mlsanity.com/paper/1903.00620/) (2019)
- [FUEL: Fast UAV Exploration using Incremental Frontier Structure and Hierarchical Planning](https://mlsanity.com/paper/2010.11561/) (2020)
- [Cascaded Context Pyramid for Full-Resolution 3D Semantic Scene Completion](https://mlsanity.com/paper/1908.00382/) (2019)
- [An Efficient Sampling-based Method for Online Informative Path Planning in Unknown Environments](https://mlsanity.com/paper/1909.09548/) (2019)
- [Panoptic Multi-TSDFs: a Flexible Representation for Online Multi-resolution Volumetric Mapping and Long-term Dynamic Scene Consistency](https://mlsanity.com/paper/2109.10165/) (2021)
- [Depth Based Semantic Scene Completion with Position Importance Aware Loss](https://mlsanity.com/paper/2001.10709/) (2020)
- [ForkNet: Multi-branch Volumetric Semantic Completion from a Single Depth Image](https://mlsanity.com/paper/1909.01106/) (2019)
- [EM-Fusion: Dynamic Object-Level SLAM with Probabilistic Data Association](https://mlsanity.com/paper/1904.11781/) (2019)
- [SCFusion: Real-time Incremental Scene Reconstruction with Semantic Completion](https://mlsanity.com/paper/2010.13662/) (2020)
- [An informative path planning framework for UAV-based terrain monitoring](https://mlsanity.com/paper/1809.03870/) (2018)
- [Fast Frontier-based Information-driven Autonomous Exploration with an MAV](https://mlsanity.com/paper/2002.04440/) (2020)
- [Learn-to-Score: Efficient 3D Scene Exploration by Predicting View Utility](https://mlsanity.com/paper/1806.10354/) (2018)
- [NeuralBlox: Real-Time Neural Representation Fusion for Robust Volumetric Mapping](https://mlsanity.com/paper/2110.09415/) (2021)
- [Volumetric Occupancy Mapping With Probabilistic Depth Completion for Robotic Navigation](https://mlsanity.com/paper/2012.03023/) (2020)
- [Embodied Active Domain Adaptation for Semantic Segmentation via Informative Path Planning](https://mlsanity.com/paper/2203.00549/) (2022)
- [Predicting Complete 3D Models of Indoor Scenes](https://mlsanity.com/paper/1504.02437/) (2015)
- [A Unified Approach for Autonomous Volumetric Exploration of Large Scale Environments under Severe Odometry Drift](https://mlsanity.com/paper/2010.09859/) (2020)
- [History-aware Autonomous Exploration in Confined Environments using MAVs](https://mlsanity.com/paper/1803.10558/) (2018)
- [Fast and Compute-efficient Sampling-based Local Exploration Planning via Distribution Learning](https://mlsanity.com/paper/2202.13715/) (2022)
- [Predicting Unobserved Space For Planning via Depth Map Augmentation](https://mlsanity.com/paper/1911.05761/) (2019)

## Built on by (most cited first, 7 of 7)

- [FrontierNet: Learning Visual Cues to Explore](https://mlsanity.com/paper/2501.04597/) (2025)
- [ForesightNav: Learning Scene Imagination for Efficient Exploration](https://mlsanity.com/paper/2504.16062/) (2025)
- [Semantically-driven Deep Reinforcement Learning for Inspection Path Planning](https://mlsanity.com/paper/2505.14443/) (2025)
- [Efficient Navigation in Unknown Indoor Environments with Vision-Language Models](https://mlsanity.com/paper/2510.04991/) (2025)
- [R2F: Repurposing Ray Frontiers for LLM-free Object Navigation](https://mlsanity.com/paper/2603.08475/) (2026)
- [FlatLands: Generative Floormap Completion From a Single Egocentric View](https://mlsanity.com/paper/2603.16016/) (2026)
- [Beyond Frontiers: Scene-Anomaly Guided Autonomous Exploration](https://mlsanity.com/paper/2607.15828/) (2026)

## Abstract (arXiv)

Exploration of unknown environments is a fundamental problem in robotics and an essential component in numerous applications of autonomous systems. A major challenge in exploring unknown environments is that the robot has to plan with the limited information available at each time step. While most current approaches rely on heuristics and assumption to plan paths based on these partial observations, we instead propose a novel way to integrate deep learning into exploration by leveraging 3D scene completion for informed, safe, and interpretable exploration mapping and planning. Our approach, SC-Explorer, combines scene completion using a novel incremental fusion mechanism and a newly proposed hierarchical multi-layer mapping approach, to guarantee safety and efficiency of the robot. We further present an informative path planning method, leveraging the capabilities of our mapping approach and a novel scene-completion-aware information gain. While our method is generally applicable, we evaluate it in the use case of a Micro Aerial Vehicle (MAV). We thoroughly study each component in high-fidelity simulation experiments using only mobile hardware, and show that our method can speed up coverage of an environment by 73% compared to the baselines with only minimal reduction in map accuracy. Even if scene completions are not included in the final map, we show that they can be used to guide the robot to choose more informative paths, speeding up the measurement of the scene with the robot's sensors by 35%. We validate our system on a fully autonomous MAV, showing rapid and reliable scene coverage even in a complex cluttered environment. We make our methods available as open-source.