P3P: Pseudo-3D Pre-training for Scaling 3D Masked Autoencoders

• URL: http://arxiv.org/abs/2408.10007v2
• Date: Wed, 12 Mar 2025 14:13:37 GMT
• Title: P3P: Pseudo-3D Pre-training for Scaling 3D Masked Autoencoders
• Authors: Xuechao Chen, Ying Chen, Jialin Li, Qiang Nie, Hanqiu Deng, Yong Liu, Qixing Huang, Yang Li,
• Abstract summary: Pre-training in 3D is pivotal for advancing 3D perception tasks.<n>However, the scarcity of clean 3D data poses significant challenges for scaling 3D pre-training efforts.<n>We introduce an innovative self-supervised pre-training framework.<n>Our method achieves state-of-the-art performance in 3D classification, detection, and few-shot learning.
• Score: 34.64343313442465
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Pre-training in 3D is pivotal for advancing 3D perception tasks. However, the scarcity of clean 3D data poses significant challenges for scaling 3D pre-training efforts. Drawing inspiration from semi-supervised learning, which effectively combines limited labeled data with abundant unlabeled data, we introduce an innovative self-supervised pre-training framework. This framework leverages both authentic 3D data and pseudo-3D data generated from images using a robust depth estimation model. Another critical challenge is the efficiency of the pre-training process. Existing approaches, such as Point-BERT and Point-MAE, utilize k-nearest neighbors for 3D token embedding, resulting in quadratic time complexity. To address this, we propose a novel token embedding strategy with linear time complexity, coupled with a training-efficient 2D reconstruction target. Our method not only achieves state-of-the-art performance in 3D classification, detection, and few-shot learning but also ensures high efficiency in both pre-training and downstream fine-tuning processes.

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