Incremental 3D Semantic Scene Graph Prediction from RGB Sequences

• URL: http://arxiv.org/abs/2305.02743v2
• Date: Sat, 6 May 2023 20:15:08 GMT
• Title: Incremental 3D Semantic Scene Graph Prediction from RGB Sequences
• Authors: Shun-Cheng Wu, Keisuke Tateno, Nassir Navab, Federico Tombari
• Abstract summary: We propose a real-time framework that incrementally builds a consistent 3D semantic scene graph of a scene given an RGB image sequence. Our method consists of a novel incremental entity estimation pipeline and a scene graph prediction network. The proposed network estimates 3D semantic scene graphs with iterative message passing using multi-view and geometric features extracted from the scene entities.
• Score: 86.77318031029404
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 3D semantic scene graphs are a powerful holistic representation as they describe the individual objects and depict the relation between them. They are compact high-level graphs that enable many tasks requiring scene reasoning. In real-world settings, existing 3D estimation methods produce robust predictions that mostly rely on dense inputs. In this work, we propose a real-time framework that incrementally builds a consistent 3D semantic scene graph of a scene given an RGB image sequence. Our method consists of a novel incremental entity estimation pipeline and a scene graph prediction network. The proposed pipeline simultaneously reconstructs a sparse point map and fuses entity estimation from the input images. The proposed network estimates 3D semantic scene graphs with iterative message passing using multi-view and geometric features extracted from the scene entities. Extensive experiments on the 3RScan dataset show the effectiveness of the proposed method in this challenging task, outperforming state-of-the-art approaches.

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