Pointy - A Lightweight Transformer for Point Cloud Foundation Models

2026-03-11

AI Summary

This work addresses the challenge of building efficient and lightweight foundation models for point cloud understanding under limited data regimes, without relying on large-scale multimodal supervision. The authors propose a lightweight, tokenizer-free Transformer architecture trained on only 39,000 point cloud samples, achieving effective representation learning through the co-optimization of network design and training strategy. Despite its minimal data footprint, the model attains performance on multiple benchmarks that is comparable to or exceeds that of state-of-the-art approaches trained on hundreds of thousands to millions of multimodal examples. These results demonstrate the efficacy of high-quality training protocols combined with streamlined backbone architectures, underscoring the critical importance of synergistically optimizing model structure and learning dynamics in data-constrained scenarios.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Foundation models for point cloud data have recently grown in capability, often leveraging extensive representation learning from language or vision. In this work, we take a more controlled approach by introducing a lightweight transformer-based point cloud architecture. In contrast to the heavy reliance on cross-modal supervision, our model is trained only on 39k point clouds - yet it outperforms several larger foundation models trained on over 200k training samples. Interestingly, our method approaches state-of-the-art results from models that have seen over a million point clouds, images, and text samples, demonstrating the value of a carefully curated training setup and architecture. To ensure rigorous evaluation, we conduct a comprehensive replication study that standardizes the training regime and benchmarks across multiple point cloud architectures. This unified experimental framework isolates the impact of architectural choices, allowing for transparent comparisons and highlighting the benefits of our design and other tokenizer-free architectures. Our results show that simple backbones can deliver competitive results to more complex or data-rich strategies. The implementation, including code, pre-trained models, and training protocols, is available at https://github.com/KonradSzafer/Pointy.

Problem

• point cloud
• foundation model
• lightweight architecture
• cross-modal supervision
• data efficiency

Innovation

• lightweight transformer
• point cloud foundation model
• tokenizer-free architecture
• controlled training setup
• replication study