PointLAMA: Latent Attention meets Mamba for Efficient Point Cloud Pretraining

2025-07-23

AI Summary

While Mamba models global point cloud structure with linear complexity, it lacks local inductive bias and thus struggles to capture fine-grained geometric features. To address this, we propose PointLAMA—a computationally efficient self-supervised pretraining framework for point clouds. First, we design a task-aware serialization strategy that integrates Hilbert and Trans-Hilbert space-filling curves with axial ordering to better preserve local structural relationships. Second, we introduce a hybrid encoder incorporating a lightweight point-wise multi-head latent attention (PMLA) module, synergistically enhancing local–global joint modeling alongside Mamba. Third, we propose a Mamba-based conditional diffusion mechanism that learns highly discriminative representations without explicit point reconstruction. Extensive experiments demonstrate that PointLAMA achieves state-of-the-art or competitive performance across multiple benchmarks—despite its remarkably low parameter count and FLOPs—validating its efficacy for efficient, self-supervised point cloud representation learning.

Technology Category

• Machine Learning: Learning with Manifolds
• Search and Optimization: Learning to Search
• Computer Vision: Diffusion Models for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• 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

Abstract

Mamba has recently gained widespread attention as a backbone model for point cloud modeling, leveraging a state-space architecture that enables efficient global sequence modeling with linear complexity. However, its lack of local inductive bias limits its capacity to capture fine-grained geometric structures in 3D data. To address this limitation, we propose extbf{PointLAMA}, a point cloud pretraining framework that combines task-aware point cloud serialization, a hybrid encoder with integrated Latent Attention and Mamba blocks, and a conditional diffusion mechanism built upon the Mamba backbone. Specifically, the task-aware point cloud serialization employs Hilbert/Trans-Hilbert space-filling curves and axis-wise sorting to structurally align point tokens for classification and segmentation tasks, respectively. Our lightweight Latent Attention block features a Point-wise Multi-head Latent Attention (PMLA) module, which is specifically designed to align with the Mamba architecture by leveraging the shared latent space characteristics of PMLA and Mamba. This enables enhanced local context modeling while preserving overall efficiency. To further enhance representation learning, we incorporate a conditional diffusion mechanism during pretraining, which denoises perturbed feature sequences without relying on explicit point-wise reconstruction. Experimental results demonstrate that PointLAMA achieves competitive performance on multiple benchmark datasets with minimal parameter count and FLOPs, validating its effectiveness for efficient point cloud pretraining.

Problem

• Enhance local geometric capture in Mamba for 3D point clouds
• Integrate latent attention with Mamba for efficient pretraining
• Improve representation via conditional diffusion without point-wise reconstruction

Innovation

• Combines Latent Attention and Mamba blocks
• Uses task-aware point cloud serialization
• Incorporates conditional diffusion mechanism