UnIRe: 動的都市シーンの教師なしインスタンス分解
UnIRe: Unsupervised Instance Decomposition for Dynamic Urban Scene Reconstruction
RGB画像とLiDAR点雲のみから、4Dスーパーポイントを用いて動的都市シーンを静的背景と個々の動的インスタンスに教師なしで分解し、3Dガウシアンスプラッティングで再構成する手法を提案。
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？
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著者: Yunxuan Mao, Rong Xiong, Yue Wang, Yiyi Liao
分類: cs.CV, cs.RO
原文アブストラクト
Reconstructing and decomposing dynamic urban scenes is crucial for autonomous driving, urban planning, and scene editing. However, existing methods fail to perform instance-aware decomposition without manual annotations, which is crucial for instance-level scene editing.We propose UnIRe, a 3D Gaussian Splatting (3DGS) based approach that decomposes a scene into a static background and individual dynamic instances using only RGB images and LiDAR point clouds. At its core, we introduce 4D superpoints, a novel representation that clusters multi-frame LiDAR points in 4D space, enabling unsupervised instance separation based on spatiotemporal correlations. These 4D superpoints serve as the foundation for our decomposed 4D initialization, i.e., providing spatial and temporal initialization to train a dynamic 3DGS for arbitrary dynamic classes without requiring bounding boxes or object templates.Furthermore, we introduce a smoothness regularization strategy in both 2D and 3D space, further improving the temporal stability.Experiments on benchmark datasets show that our method outperforms existing methods in decomposed dynamic scene reconstruction while enabling accurate and flexible instance-level editing, making it a practical solution for real-world applications.
