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FreePoint: Unsupervised Point Cloud Instance Segmentation by Zhang, Zhikai; Ding, Jian; Jiang, Li; Dai, Dengxin; Xia, Gui-Song is a scholarly article available to read on EtoBox.

What is FreePoint: Unsupervised Point Cloud Instance Segmentation about?

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 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 trad

Author

Zhang, Zhikai; Ding, Jian; Jiang, Li; Dai, Dengxin; Xia, Gui-Song

Published

2023

Language

EN