3Dシーングラフ予測：部分観測環境からの階層モデル生成
3D Scene Graph Prediction: Generating Hierarchical Models from Partially Observed Environments
ロボットが環境の一部を探索した際に、未探索部分を予測するための階層的な3Dシーングラフ生成フレームワークを提案した。部屋レイヤーとオブジェクトレイヤーを分離し、拡散モデルを用いて部屋のカテゴリ・床境界・移動可能性と、各室内のオブジェクト配置を予測する。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Siyi Hu, Jared Strader, Hyungtae Lim, Luca Carlone
分類: cs.RO, cs.CV
原文アブストラクト
Generating realistic 3D indoor scenes is an area of growing interest in computer vision and robotics. Existing methods, often motivated by applications such as interior design, generally focus on object layout generation within a single room. The generation of high-level scene structure, such as room-level layout and traversability, remains underexplored despite its importance for robotics applications. In this paper, we consider the case where a robot has explored part of an environment and needs to predict the unexplored parts to support downstream tasks such as exploration or object search. We propose a top-down framework for synthesizing hierarchical 3D scene graphs, including a room layer -- describing the floor plan and traversability -- and an object layer modeling object layouts within each room. For the room layer, we propose a novel mixed-domain graph diffusion model jointly predicting room categories, floor boundaries, and traversability between rooms. Via corruption and masking, this model supports partial constraints such as incomplete floor plans, avoiding the need for partially observed training data. For the object layer, we integrate an existing mixed discrete-continuous diffusion model for joint prediction of object categories, locations, sizes, and orientations within each room given the floor plan. We compare our method with state-of-the-art occupancy-based and LLM-based floor plan generation methods on a standard benchmark. Compared with an occupancy-based learning baseline, our method generalizes substantially better to out-of-distribution partial floor plans. We also demonstrate our integrated prediction pipeline on real-world scenes from robot-collected data, enabling prediction beyond explored areas.
