NeurIPS 2025

Complete Structure Guided Point Cloud Completion
via Cluster- and Instance-Level Contrastive Learning
Yang Chen1, Yirun Zhou1, Weizhong Zhang2,3, Cheng Jin1,3∗
1College of Computer Science and Artificial Intelligence
2School of Data Science, Fudan University
3Shanghai Key Laboratory of Intelligent Information Processing
{chen_yang23,yrzhou22}@m.fudan.edu.cn, {weizhongzhang,jc}@fudan.edu.cn
Abstract
Point cloud completion, aiming to reconstruct missing part from incomplete point
clouds, is a pivotal task in 3D computer vision. Traditional supervised approaches
often necessitate complete point clouds for training supervision, which are not
readily accessible in real-world applications. Recent studies have attempted to
mitigate this dependency by employing self-supervise mechanisms. However, these
approaches frequently yield suboptimal results due to the absence of complete
structure in the point cloud data during training. To address these issues, in this
paper, we propose an effective framework to complete the point cloud under the
guidance of self learned complete structure. A key contribution of our work
is the development of a novel self-supervised complete structure reconstruction
module, which can learn the complete structure explicitly from incomplete point
clouds and thus eliminate the reliance on training data from complete point clouds.
Additionally, we introduce a contrastive learning approach at both the cluster-
and instance-level to extract shape features guided by the complete structure and
to capture style features, respectively. This dual-level learning design ensures
that the generated point clouds are both shape-completed and detail-preserving.
Extensive experiments on both synthetic and real-world datasets demonstrate that
our approach significantly outperforms state-of-the-art self-supervised methods.
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Introduction
The advancement and widespread adoption of 3D sensors, particularly LiDAR, have led to the
emergence of point clouds as a dominant re...