# UnitedVLN: Generalizable Gaussian Splatting for Continuous Vision-Language Navigation

arXiv: https://arxiv.org/abs/2411.16053 · HTML: https://mlsanity.com/paper/2411.16053/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2024.

## Built on by (most cited first, 10 of 10)

- [NavMorph: A Self-Evolving World Model for Vision-and-Language Navigation in Continuous Environments](https://mlsanity.com/paper/2506.23468/) (2025)
- [Cross from Left to Right Brain: Adaptive Text Dreamer for Vision-and-Language Navigation](https://mlsanity.com/paper/2505.20897/) (2025)
- [DreamNav: A Trajectory-Based Imaginative Framework for Zero-Shot Vision-and-Language Navigation](https://mlsanity.com/paper/2509.11197/) (2025)
- [MonoDream: Monocular Vision-Language Navigation with Panoramic Dreaming](https://mlsanity.com/paper/2508.02549/) (2025)
- [Segment then Splat: Unified 3D Open-Vocabulary Segmentation via Gaussian Splatting](https://mlsanity.com/paper/2503.22204/) (2025)
- [View Invariant Learning for Vision-Language Navigation in Continuous Environments](https://mlsanity.com/paper/2507.08831/) (2025)
- [Feed-Forward 3D Scene Modeling: A Problem-Driven Perspective](https://mlsanity.com/paper/2604.14025/) (2026)
- [textscNaVIDA: Vision-Language Navigation with Inverse Dynamics Augmentation](https://mlsanity.com/paper/2601.18188/) (2026)
- [SC2-WM: A Self-Correcting World Model with Closed-Loop Feedback for Vision-and-Language Navigation in Continuous Environments](https://mlsanity.com/paper/2608.07548/) (2026)
- [Instance-Enriched Semantic Maps for Visual Language Navigation](https://mlsanity.com/paper/2607.12630/) (2026)

## Abstract (arXiv)

Vision-and-Language Navigation (VLN), where an agent follows instructions to reach a target destination, has recently seen significant advancements. In contrast to navigation in discrete environments with predefined trajectories, VLN in Continuous Environments (VLN-CE) presents greater challenges, as the agent is free to navigate any unobstructed location and is more vulnerable to visual occlusions or blind spots. Recent approaches have attempted to address this by imagining future environments, either through predicted future visual images or semantic features, rather than relying solely on current observations. However, these RGB-based and feature-based methods lack intuitive appearance-level information or high-level semantic complexity crucial for effective navigation. To overcome these limitations, we introduce a novel, generalizable 3DGS-based pre-training paradigm, called UnitedVLN, which enables agents to better explore future environments by unitedly rendering high-fidelity 360 visual images and semantic features. UnitedVLN employs two key schemes: search-then-query sampling and separate-then-united rendering, which facilitate efficient exploitation of neural primitives, helping to integrate both appearance and semantic information for more robust navigation. Extensive experiments demonstrate that UnitedVLN outperforms state-of-the-art methods on existing VLN-CE benchmarks.