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

TL;DR

MAP-ADAPT is a real-time semantic 3D mapping method that adaptively adjusts map quality based on scene complexity, reducing computational costs while maintaining high accuracy for autonomous applications.

Contribution

It introduces the first adaptive semantic 3D mapping algorithm that creates a single map with variable quality regions based on semantic and geometric cues.

Findings

Achieves comparable or better accuracy than state-of-the-art methods.

Reduces storage and computation costs significantly.

Operates in real-time on synthetic and real-world data.

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.