A coarse-to-fine point completion network with details compensation and structure enhancement.

Point cloud completion, the issue of estimating the complete geometry of objects from partially-scanned point cloud data, becomes a fundamental task in many 3d vision and robotics applications. To address the limitations on inadequate prediction of shape details for traditional methods, a novel coarse-to-fine point completion network (DCSE-PCN) is introduced in this work using the modules of local details compensation and shape structure enhancement for effective geometric learning. The coarse completion stage of our network consists of two branches-a shape structure recovery branch and a local details compensation branch, which can recover the overall shape of the underlying model and the shape details of incomplete point cloud through feature learning and hierarchical feature fusion. The fine completion stage of our network employs the structure enhancement module to reinforce the correlated shape structures of the coarse repaired shape (such as regular arrangement or symmetry), thus obtaining the completed geometric shape with finer-grained details. Extensive experimental results on ShapeNet dataset and ModelNet dataset validate the effectiveness of our completion network, which can recover the shape details of the underlying point cloud whilst maintaining its overall shape. Compared to the existing methods, our coarse-to-fine completion network has shown its competitive performance on both chamfer distance (CD) and earth mover distance (EMD) errors. Such as, the repairing results on the ShapeNet dataset of our completion network are reduced by an average of [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] in terms of CD error, comparing with PCN, FoldingNet, Atlas, and CRN methods, respectively; and also reduced by an average of [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] in terms of EMD error, respectively. Meanwhile, the completion results on the ModelNet dataset of our network have an average reduction of [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] in terms of CD error, comparing to PCN, FoldingNet, Atlas, and CRN methods, respectively; and also an average reduction of [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] in terms of EMD error, respectively. Our proposed point completion network is also robust to different degrees of data incompleteness and model noise.