  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.
