# MAP-ADAPT: Real-Time Quality-Adaptive Semantic 3D Maps

arXiv: https://arxiv.org/abs/2406.05849 · HTML: https://mlsanity.com/paper/2406.05849/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2024.

## Built on by (most cited first, 5 of 5)

- [P2P-Bridge: Diffusion Bridges for 3D Point Cloud Denoising](https://mlsanity.com/paper/2408.16325/) (2024)
- [OVI-MAP:Open-Vocabulary Instance-Semantic Mapping](https://mlsanity.com/paper/2603.26541/) (2026)
- [The Semantic Lifecycle in Embodied AI: Acquisition, Representation and Storage via Foundation Models](https://mlsanity.com/paper/2601.08876/) (2026)
- [Object-X: Learning to Reconstruct Multi-Modal 3D Object Representations](https://mlsanity.com/paper/2506.04789/) (2025)
- [Uncertainty-Aware RL-Controlled Adaptive 3D Mapping](https://mlsanity.com/paper/2610.00188/) (2026)

## Abstract (arXiv)

Creating 3D semantic reconstructions of environments is fundamental to many applications, especially when related to autonomous agent operation (e.g., goal-oriented navigation or object interaction and manipulation). Commonly, 3D semantic reconstruction systems capture the entire scene in the same level of detail. However, certain tasks (e.g., object interaction) require a fine-grained and high-resolution map, particularly if the objects to interact are of small size or intricate geometry. In recent practice, this leads to the entire map being in the same high-quality resolution, which results in increased computational and storage costs. To address this challenge, we propose MAP-ADAPT, a real-time method for quality-adaptive semantic 3D reconstruction using RGBD frames. MAP-ADAPT is the first adaptive semantic 3D mapping algorithm that, unlike prior work, generates directly a single map with regions of different quality based on both the semantic information and the geometric complexity of the scene. Leveraging a semantic SLAM pipeline for pose and semantic estimation, we achieve comparable or superior results to state-of-the-art methods on synthetic and real-world data, while significantly reducing storage and computation requirements.