# FrontierNet: Learning Visual Cues to Explore

arXiv: https://arxiv.org/abs/2501.04597 · HTML: https://mlsanity.com/paper/2501.04597/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 47 references that are in the map.
- First posted on arXiv 8 Jan 2025.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Open3D: A Modern Library for 3D Data Processing](https://mlsanity.com/paper/1801.09847/) (2018)
- [AdaBins: Depth Estimation using Adaptive Bins](https://mlsanity.com/paper/2011.14141/) (2020)
- [Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI](https://mlsanity.com/paper/2109.08238/) (2021)
- [Metric3Dv2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimation](https://mlsanity.com/paper/2404.15506/) (2024)
- [ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects](https://mlsanity.com/paper/2006.13171/) (2020)
- [VLFM: Vision-Language Frontier Maps for Zero-Shot Semantic Navigation](https://mlsanity.com/paper/2312.03275/) (2023)
- [Holistically-Attracted Wireframe Parsing](https://mlsanity.com/paper/2003.01663/) (2020)
- [FisherRF: Active View Selection and Uncertainty Quantification for Radiance Fields using Fisher Information](https://mlsanity.com/paper/2311.17874/) (2023)
- [DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients](https://mlsanity.com/paper/2212.07766/) (2022)
- [Uncertainty Guided Policy for Active Robotic 3D Reconstruction using Neural Radiance Fields](https://mlsanity.com/paper/2209.08409/) (2022)
- [How To Not Train Your Dragon: Training-free Embodied Object Goal Navigation with Semantic Frontiers](https://mlsanity.com/paper/2305.16925/) (2023)
- [FUEL: Fast UAV Exploration using Incremental Frontier Structure and Hierarchical Planning](https://mlsanity.com/paper/2010.11561/) (2020)
- [SEAL: Self-supervised Embodied Active Learning using Exploration and 3D Consistency](https://mlsanity.com/paper/2112.01001/) (2021)
- [An Efficient Sampling-based Method for Online Informative Path Planning in Unknown Environments](https://mlsanity.com/paper/1909.09548/) (2019)
- [Active Neural Mapping](https://mlsanity.com/paper/2308.16246/) (2023)
- [Fast Frontier-based Information-driven Autonomous Exploration with an MAV](https://mlsanity.com/paper/2002.04440/) (2020)
- [Active Neural Localization](https://mlsanity.com/paper/1801.08214/) (2018)
- [RT-GuIDE: Real-Time Gaussian Splatting for Information-Driven Exploration](https://mlsanity.com/paper/2409.18122/) (2024)
- [SEER: Safe Efficient Exploration for Aerial Robots using Learning to Predict Information Gain](https://mlsanity.com/paper/2209.11034/) (2022)
- [Finding Things in the Unknown: Semantic Object-Centric Exploration with an MAV](https://mlsanity.com/paper/2302.14569/) (2023)
- [A Unified Approach for Autonomous Volumetric Exploration of Large Scale Environments under Severe Odometry Drift](https://mlsanity.com/paper/2010.09859/) (2020)
- [Active Bayesian Multi-class Mapping from Range and Semantic Segmentation Observation](https://mlsanity.com/paper/2101.01831/) (2021)
- [SC-Explorer: Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and Planning](https://mlsanity.com/paper/2208.08307/) (2022)
- [Semantic Belief Behavior Graph: Enabling Autonomous Robot Inspection in Unknown Environments](https://mlsanity.com/paper/2401.17191/) (2024)
- [3D Active Metric-Semantic SLAM](https://mlsanity.com/paper/2309.06950/) (2023)
- [A 3D Mixed Reality Interface for Human-Robot Teaming](https://mlsanity.com/paper/2310.02392/) (2023)
- [IPPON: Common Sense Guided Informative Path Planning for Object Goal Navigation](https://mlsanity.com/paper/2410.19697/) (2024)
- [Efficient Submap-based Autonomous MAV Exploration using Visual-Inertial SLAM Configurable for LiDARs or Depth Cameras](https://mlsanity.com/paper/2409.16972/) (2024)

## Built on by (most cited first, 12 of 12)

- [History-Augmented Vision-Language Models for Frontier-Based Zero-Shot Object Navigation](https://mlsanity.com/paper/2506.16623/) (2025)
- [OpenFrontier: General Navigation with Visual-Language Grounded Frontiers](https://mlsanity.com/paper/2603.05377/) (2026)
- [Explore Like Humans: Autonomous Exploration with Online SG-Memo Construction for Embodied Agents](https://mlsanity.com/paper/2604.19034/) (2026)
- [HyPerNav: Hybrid Perception for Object-Oriented Navigation in Unknown Environment](https://mlsanity.com/paper/2510.22917/) (2025)
- [LIME: Learning Intent-aware Camera Motion from Egocentric Video](https://mlsanity.com/paper/2607.02417/) (2026)
- [Sight Over Site: Perception-Aware Reinforcement Learning for Efficient Robotic Inspection](https://mlsanity.com/paper/2509.17877/) (2025)
- [When Engineering Outruns Intelligence: Rethinking Instruction-Guided Navigation](https://mlsanity.com/paper/2507.20021/) (2025)
- [AION: Aerial Indoor Object-Goal Navigation Using Dual-Policy Reinforcement Learning](https://mlsanity.com/paper/2601.15614/) (2026)
- [R2F: Repurposing Ray Frontiers for LLM-free Object Navigation](https://mlsanity.com/paper/2603.08475/) (2026)
- [WISE: A Long-Horizon Agent in Minecraft with Why-Which Reasoning](https://mlsanity.com/paper/2606.12852/) (2026)
- [EffiNav: Fusing Depth and Vision-Language for Efficient Object Goal Navigation](https://mlsanity.com/paper/2606.18634/) (2026)
- [Beyond Frontiers: Scene-Anomaly Guided Autonomous Exploration](https://mlsanity.com/paper/2607.15828/) (2026)

## Abstract (arXiv)

Exploration of unknown environments is crucial for autonomous robots; it allows them to actively reason and decide on what new data to acquire for different tasks, such as mapping, object discovery, and environmental assessment. Existing solutions, such as frontier-based exploration approaches, rely heavily on 3D map operations, which are limited by map quality and, more critically, often overlook valuable context from visual cues. This work aims at leveraging 2D visual cues for efficient autonomous exploration, addressing the limitations of extracting goal poses from a 3D map. We propose a visual-only frontier-based exploration system, with FrontierNet as its core component. FrontierNet is a learning-based model that (i) proposes frontiers, and (ii) predicts their information gain, from posed RGB images enhanced by monocular depth priors. Our approach provides an alternative to existing 3D-dependent goal-extraction approaches, achieving a 15% improvement in early-stage exploration efficiency, as validated through extensive simulations and real-world experiments. The project is available at https://github.com/cvg/FrontierNet.