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

Jianhao Zheng*, Daniel Barath, Marc Pollefeys, Iro Armeni* ;

"Creating 3D semantic reconstructions of environments is fundamental to many applications, especially when related to autonomous agent operation (, 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 (, 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 , a real-time method for quality-adaptive semantic 3D reconstruction using RGBD frames. 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. Code is available at eccvpinkhttps://map-adapt.github.io/."