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arXiv · 2406.05849

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

Jianhao Zheng·Daniel Barath·Marc Pollefeys·Iro Armeni
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Abstract

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.

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Jianhao Zheng, Daniel Barath, Marc Pollefeys, Iro Armeni. 2024-06-09. MAP-ADAPT: Real-Time Quality-Adaptive Semantic 3D Maps. https://arxiv.org/abs/2406.05849

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