FreePoint: Unsupervised Point Cloud Instance Segmentation

Zhikai Zhang, Jian Ding, Li Jiang, Dengxin Dai, Guisong Xia; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 28254-28263

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 time-consuming and expensive. To alleviate dependency on annotations we propose a novel framework FreePoint for underexplored unsupervised class-agnostic instance segmentation on point clouds. In detail we represent the point features by combining coordinates colors and self-supervised deep features. Based on the point features we perform a bottom-up multicut algorithm to segment point clouds into coarse instance masks as pseudo labels which are used to train a point cloud instance segmentation model. We propose an id-as-feature strategy at this stage to alleviate the randomness of the multicut algorithm and improve the pseudo labels' quality. During training we propose a weakly-supervised two-step training strategy and corresponding losses to overcome the inaccuracy of coarse masks. FreePoint has achieved breakthroughs in unsupervised class-agnostic instance segmentation on point clouds and outperformed previous traditional methods by over 18.2% and a competitive concurrent work UnScene3D by 5.5% in AP. Additionally when used as a pretext task and fine-tuned on S3DIS FreePoint performs significantly better than existing self-supervised pre-training methods with limited annotations and surpasses CSC by 6.0% in AP with 10% annotation masks. Code will be released at https://github.com/zzk273/FreePoint.