无需场景标注，通过生成式先验实现复杂点云中物体的精准分割。
GrabS: Generative Embodied Agent for 3D Object Segmentation without Scene Supervision
- 两阶段框架：先学习物体生成与判别先验，再用具身智能体查询发现物体。
- 在真实与合成数据集上表现远超现有无监督方法，分割精度显著提升。
- 适合做无标注3D场景理解、机器人感知等任务的研究者参考。
我们研究在复杂点云中进行3D物体分割的难题，且不依赖人类标注的3D场景监督信号。现有无监督方法通常依赖预训练2D特征或外部信号（如运动）来聚类3D点，但受限于预训练特征中缺乏对象性（objectness），常只能识别简单物体，分割效果较差。本文提出一种名为GrabS的新两阶段流程：第一阶段从物体数据集中学习生成性与判别性物体中心先验；第二阶段设计一个具身智能体，通过向预训练生成先验发起查询来学习发现多个物体。我们在两个真实世界数据集和一个新构建的合成数据集上进行了广泛评估，结果表明该方法展现出卓越的分割性能，明显优于所有现有无监督方法。
原文摘要 · Abstract (English)
We study the hard problem of 3D object segmentation in complex point clouds without requiring human labels of 3D scenes for supervision. By relying on the similarity of pretrained 2D features or external signals such as motion to group 3D points as objects, existing unsupervised methods are usually limited to identifying simple objects like cars or their segmented objects are often inferior due to the lack of objectness in pretrained features. In this paper, we propose a new two-stage pipeline called GrabS. The core concept of our method is to learn generative and discriminative object-centric priors as a foundation from object datasets in the first stage, and then design an embodied agent to learn to discover multiple objects by querying against the pretrained generative priors in the second stage. We extensively evaluate our method on two real-world datasets and a newly created synthetic dataset, demonstrating remarkable segmentation performance, clearly surpassing all existing unsupervised methods.
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