Towards Foundation Models for 3D Scene Understanding: Instance-Aware Self-Supervised Learning for Point Clouds

2026-03-26

AI Summary

This work addresses the limited instance-awareness of existing self-supervised point cloud learning methods, which hinders their transferability to instance localization tasks and necessitates full fine-tuning. To overcome this, we propose PointINS, the first framework to incorporate instance awareness into self-supervised point cloud representation learning through geometry-aware joint modeling that synergistically optimizes semantic understanding and geometric reasoning. Our key innovations include an orthogonal offset branch, Offset Distribution Regularization (ODR), and Spatial Clustering Regularization (SCR), all guided by pseudo-instance masks. Extensive experiments demonstrate significant performance gains across five datasets: indoor instance segmentation achieves an average 3.5% mAP improvement, and outdoor panoptic segmentation shows a 4.1% PQ gain, substantially advancing the development of 3D foundation models.

Technology Category

• Machine Learning: Unsupervised & Self-Supervised Learning
• Search and Optimization: Learning to Search
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Data transparency and provenance

Abstract

Recent advances in self-supervised learning (SSL) for point clouds have substantially improved 3D scene understanding without human annotations. Existing approaches emphasize semantic awareness by enforcing feature consistency across augmented views or by masked scene modeling. However, the resulting representations transfer poorly to instance localization, and often require full finetuning for strong performance. Instance awareness is a fundamental component of 3D perception, thus bridging this gap is crucial for progressing toward true 3D foundation models that support all downstream tasks on 3D data. In this work, we introduce PointINS, an instance-oriented self-supervised framework that enriches point cloud representations through geometry-aware learning. PointINS employs an orthogonal offset branch to jointly learn high-level semantic understanding and geometric reasoning, yielding instance awareness. We identify two consistent properties essential for robust instance localization and formulate them as complementary regularization strategies, Offset Distribution Regularization (ODR), which aligns predicted offsets with empirically observed geometric priors, and Spatial Clustering Regularization (SCR), which enforces local coherence by regularizing offsets with pseudo-instance masks. Through extensive experiments across five datasets, PointINS achieves on average +3.5% mAP improvement for indoor instance segmentation and +4.1% PQ gain for outdoor panoptic segmentation, paving the way for scalable 3D foundation models.

Problem

• instance awareness
• 3D scene understanding
• self-supervised learning
• point clouds
• foundation models

Innovation

• instance-aware
• self-supervised learning
• point cloud
• geometric reasoning
• foundation model