Self-Supervised Point Cloud Completion via Inpainting

Abstract

When navigating in urban environments, many of the objects that need to be tracked and avoided are heavily occluded. Planning and tracking using these partial scans can be challenging. The aim of this work is to learn to complete these partial point clouds, giving us a full understanding of the object's geometry using only partial observations. Previous methods achieve this with the help of complete, ground-truth annotations of the target objects, which are available only for simulated datasets. However, such ground truth is unavailable for real-world LiDAR data. In this work, we present a self-supervised point cloud completion algorithm, PointPnCNet, which is trained only on partial scans without assuming access to complete, ground-truth annotations. Our method achieves this via inpainting. We remove a portion of the input data and train the network to complete the missing region. As it is difficult to determine which regions were occluded in the initial cloud and which were synthetically removed, our network learns to complete the full cloud, including the missing regions in the initial partial cloud. We show that our method outperforms previous unsupervised and weakly-supervised methods on both the synthetic dataset, ShapeNet, and real-world LiDAR dataset, Semantic KITTI.

Point Cloud Completion: A Survey

“…The auto-encoding learns the basic shape of the object and uses it to fool the discriminator by minimising the gap between realscene and artificial data. PointPnCNet [101] uses inpainting, where a portion of the input data is removed and the network trains to complete the point cloud including the missing region. PointPnCNet is trained only on partial inputs.…”

A Partial Point Cloud Completion Network Focusing on Detail Reconstruction

“…Points are fused in a new dimension. In addition, the attention mechanism is used to weight the processing, making more important parts highlighted, and multiple branches are added to enrich the fusion level [36,37]. The structure is inspired by CC-Net [38].…”

Cyclic Global Guiding Network for Point Cloud Completion

“…It expresses the spatial distribution and surface features of the target. However, the sparsity of point clouds has always been a serious problem for radar perception [4,5]. How to make sparse point clouds dense and complete is the focus of research [6,7].…”