  In real-world scenarios, scanned point clouds are often incomplete due to
occlusion issues. The task of self-supervised point cloud completion involves
reconstructing missing regions of these incomplete objects without the
supervision of complete ground truth. Current self-supervised methods either
rely on multiple views of partial observations for supervision or overlook the
intrinsic geometric similarity that can be identified and utilized from the
given partial point clouds. In this paper, we propose MAL-SPC, a framework that
effectively leverages both object-level and category-specific geometric
similarities to complete missing structures. Our MAL-SPC does not require any
3D complete supervision and only necessitates a single partial point cloud for
each object. Specifically, we first introduce a Pattern Retrieval Network to
retrieve similar position and curvature patterns between the partial input and
the predicted shape, then leverage these similarities to densify and refine the
reconstructed results. Additionally, we render the reconstructed complete shape
into multi-view depth maps and design an adversarial learning module to learn
the geometry of the target shape from category-specific single-view depth
images. To achieve anisotropic rendering, we design a density-aware radius
estimation algorithm to improve the quality of the rendered images. Our MAL-SPC
yields the best results compared to current state-of-the-art methods.We will
make the source code publicly available at \url{https://github.com/ltwu6/malspc
