用2D视觉模型生成3D实例分割，实时高效且无需训练。
OnlineAnySeg: Online Zero-Shot 3D Segmentation by Visual Foundation Model Guided 2D Mask Merging
- 通过哈希技术将2D掩码映射到3D空间，实现快速匹配。
- 在ScanNet和SceneNN上达到领先性能，推理速度提升10倍以上。
- 适合机器人、AR等需要实时3D理解的场景应用。
在持续构建的场景中进行在线零样本3D实例分割，对具身智能应用至关重要且极具挑战。随着视觉基础模型（VFMs）在图像领域的成功，利用2D先验解决3D在线分割成为研究热点。由于2D先验提供的分割结果需保持空间一致性才能转化为最终3D分割，高效识别2D掩码间的空间重叠至关重要——但现有方法极少能在实时条件下完成，多局限于离线处理。为此，我们提出一种高效方法，通过哈希技术将VFMs生成的2D掩码统一映射至3D空间。采用体素哈希实现高效的3D场景查询，将昂贵的空间重叠查询时间复杂度从$O(n^2)$降至$O(n)$。精确的空间关联使3D掩码合并可通过简单相似性过滤在零样本条件下完成，显著提升对不完整与噪声数据的鲁棒性。在ScanNet和SceneNN基准上的评估表明，本方法在在线零样本3D实例分割任务中达到当前最优性能，兼具领先效率。
原文摘要 · Abstract (English)
Online zero-shot 3D instance segmentation of a progressively reconstructed scene is both a critical and challenging task for embodied applications. With the success of visual foundation models (VFMs) in the image domain, leveraging 2D priors to address 3D online segmentation has become a prominent research focus. Since segmentation results provided by 2D priors often require spatial consistency to be lifted into final 3D segmentation, an efficient method for identifying spatial overlap among 2D masks is essential - yet existing methods rarely achieve this in real time, mainly limiting its use to offline approaches. To address this, we propose an efficient method that lifts 2D masks generated by VFMs into a unified 3D instance using a hashing technique. By employing voxel hashing for efficient 3D scene querying, our approach reduces the time complexity of costly spatial overlap queries from $O(n^2)$ to $O(n)$. Accurate spatial associations further enable 3D merging of 2D masks through simple similarity-based filtering in a zero-shot manner, making our approach more robust to incomplete and noisy data. Evaluated on the ScanNet and SceneNN benchmarks, our approach achieves state-of-the-art performance in online, zero-shot 3D instance segmentation with leading efficiency.
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