Instance segmentation of point clouds is a crucial task in 3D field with numerous applications that involve localizing and segmenting objects in a scene. However, achieving satisfactory results requires a large number of manual annotations, which is a time-consuming and expensive process. To alleviate dependency on annotations, we propose a method, called FreePoint, for underexplored unsupervised class-agnostic instance segmentation on point clouds. In detail, we represent the point features by combining coordinates, colors, normals, and self-supervised deep features. Based on the point features, we perform a multicut algorithm to segment point clouds into coarse instance masks as pseudo labels, which are used to train a point cloud instance segmentation model. To alleviate the inaccuracy of coarse masks during training, we propose a weakly-supervised training strategy and corresponding loss. Our work can also serve as an unsupervised pre-training pretext for supervised semantic instance segmentation with limited annotations. For class-agnostic instance segmentation on point clouds, FreePoint largely fills the gap with its fully-supervised counterpart based on the state-of-the-art instance segmentation model Mask3D and even surpasses some previous fully-supervised methods. When serving as a pretext task and fine-tuning on S3DIS, FreePoint outperforms training from scratch by 5.8% AP with only 10% mask annotations.

翻译：点云实例分割是三维领域中的一项关键任务，在场景中定位和分割物体方面具有众多应用。然而，取得令人满意的结果需要大量人工标注，这是一个耗时且昂贵的过程。为减轻对标注的依赖，我们提出了一种名为FreePoint的方法，用于解决探索不足的点云上无监督类不可知实例分割问题。具体而言，我们通过组合坐标、颜色、法向量和自监督深度特征来表示点特征。基于这些点特征，我们采用多切割算法将点云分割为粗粒度的实例掩膜作为伪标签，用于训练点云实例分割模型。为缓解训练中粗粒度掩膜的不准确性，我们提出了一种弱监督训练策略及相应的损失函数。我们的工作还可作为无监督预训练任务，用于有限标注下的监督式语义实例分割。对于点云上的类不可知实例分割，FreePoint基于当前最先进的实例分割模型Mask3D，大幅缩小了与其全监督对应方法的差距，甚至超越了某些先前全监督方法。当作为预训练任务并在S3DIS数据集上微调时，FreePoint仅使用10%的掩膜标注就比从零训练高出5.8%的AP。