# OmniMap: A General Mapping Framework Integrating Optics, Geometry, and Semantics

arXiv: https://arxiv.org/abs/2509.07500 · HTML: https://mlsanity.com/paper/2509.07500/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2025.

## Built on by (most cited first, 9 of 9)

- [GaussianDWM: 3D Gaussian Driving World Model for Unified Scene Understanding and Multi-Modal Generation](https://mlsanity.com/paper/2512.23180/) (2025)
- [UniGS: Unified Geometry-Aware Gaussian Splatting for Multimodal Rendering](https://mlsanity.com/paper/2510.12174/) (2025)
- [Towards Next-Generation SLAM: A Survey on 3DGS-SLAM Focusing on Performance, Robustness, and Future Directions](https://mlsanity.com/paper/2602.04251/) (2026)
- [IRIS-SLAM: Unified Geo-Instance Representations for Robust Semantic Localization and Mapping](https://mlsanity.com/paper/2602.18709/) (2026)
- [PhysGraph: A Physics-aware 3D Scene Graph for Perception and Reasoning](https://mlsanity.com/paper/2606.08655/) (2026)
- [LatentAM: Real-Time, Large-Scale Latent Gaussian Attention Mapping via Online Dictionary Learning](https://mlsanity.com/paper/2602.12314/) (2026)
- [S2GS: Streaming Semantic Gaussian Splatting for Online Scene Understanding and Reconstruction](https://mlsanity.com/paper/2603.14232/) (2026)
- [DGSG-Mind: Dynamic 3D Gaussian Scene Graphs for Long-Term Scene Understanding and Grounding](https://mlsanity.com/paper/2605.29879/) (2026)
- [GaussianDWM++: Language-Grounded 3D Gaussian Driving World Model for Unified Scene Understanding, Editing, and Multi-Modal Generation](https://mlsanity.com/paper/2608.16234/) (2026)

## Abstract (arXiv)

Robotic systems demand accurate and comprehensive 3D environment perception, requiring simultaneous capture of photo-realistic appearance (optical), precise layout shape (geometric), and open-vocabulary scene understanding (semantic). Existing methods typically achieve only partial fulfillment of these requirements while exhibiting optical blurring, geometric irregularities, and semantic ambiguities. To address these challenges, we propose OmniMap. Overall, OmniMap represents the first online mapping framework that simultaneously captures optical, geometric, and semantic scene attributes while maintaining real-time performance and model compactness. At the architectural level, OmniMap employs a tightly coupled 3DGS-Voxel hybrid representation that combines fine-grained modeling with structural stability. At the implementation level, OmniMap identifies key challenges across different modalities and introduces several innovations: adaptive camera modeling for motion blur and exposure compensation, hybrid incremental representation with normal constraints, and probabilistic fusion for robust instance-level understanding. Extensive experiments show OmniMap's superior performance in rendering fidelity, geometric accuracy, and zero-shot semantic segmentation compared to state-of-the-art methods across diverse scenes. The framework's versatility is further evidenced through a variety of downstream applications, including multi-domain scene Q&A, interactive editing, perception-guided manipulation, and map-assisted navigation.