  Self-supervised learning has not been fully explored for point cloud
analysis. Current frameworks are mainly based on point cloud reconstruction.
Given only 3D coordinates, such approaches tend to learn local geometric
structures and contours, while failing in understanding high level semantic
content. Consequently, they achieve unsatisfactory performance in downstream
tasks such as classification, segmentation, etc. To fill this gap, we propose a
generic Contour-Perturbed Reconstruction Network (CP-Net), which can
effectively guide self-supervised reconstruction to learn semantic content in
the point cloud, and thus promote discriminative power of point cloud
representation. First, we introduce a concise contour-perturbed augmentation
module for point cloud reconstruction. With guidance of geometry disentangling,
we divide point cloud into contour and content components. Subsequently, we
perturb the contour components and preserve the content components on the point
cloud. As a result, self supervisor can effectively focus on semantic content,
by reconstructing the original point cloud from such perturbed one. Second, we
use this perturbed reconstruction as an assistant branch, to guide the learning
of basic reconstruction branch via a distinct dual-branch consistency loss. In
this case, our CP-Net not only captures structural contour but also learn
semantic content for discriminative downstream tasks. Finally, we perform
extensive experiments on a number of point cloud benchmarks. Part segmentation
results demonstrate that our CP-Net (81.5% of mIoU) outperforms the previous
self-supervised models, and narrows the gap with the fully-supervised methods.
For classification, we get a competitive result with the fully-supervised
methods on ModelNet40 (92.5% accuracy) and ScanObjectNN (87.9% accuracy). The
codes and models will be released afterwards.
