ODAM: Object Detection, Association, and Mapping using Posed RGB Video

• URL: http://arxiv.org/abs/2108.10165v1
• Date: Mon, 23 Aug 2021 13:28:10 GMT
• Title: ODAM: Object Detection, Association, and Mapping using Posed RGB Video
• Authors: Kejie Li, Daniel DeTone, Steven Chen, Minh Vo, Ian Reid, Hamid Rezatofighi, Chris Sweeney, Julian Straub, Richard Newcombe
• Abstract summary: We present ODAM, a system for 3D Object Detection, Association, and Mapping using posed RGB videos. The proposed system relies on a deep learning front-end to detect 3D objects from a given RGB frame and associate them to a global object-based map using a graph neural network (GNN)
• Score: 36.16010611723447
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Localizing objects and estimating their extent in 3D is an important step towards high-level 3D scene understanding, which has many applications in Augmented Reality and Robotics. We present ODAM, a system for 3D Object Detection, Association, and Mapping using posed RGB videos. The proposed system relies on a deep learning front-end to detect 3D objects from a given RGB frame and associate them to a global object-based map using a graph neural network (GNN). Based on these frame-to-model associations, our back-end optimizes object bounding volumes, represented as super-quadrics, under multi-view geometry constraints and the object scale prior. We validate the proposed system on ScanNet where we show a significant improvement over existing RGB-only methods.

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