FreePoint: Unsupervised Point Cloud Instance Segmentation
Zhikai Zhang Jian Ding Li Jiang Dengxin Dai Gui-Song Xia
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.