PseudoNeg-MAE: Self-Supervised Point Cloud Learning using Conditional Pseudo-Negative Embeddings

• URL: http://arxiv.org/abs/2409.15832v1
• Date: Tue, 24 Sep 2024 07:57:21 GMT
• Title: PseudoNeg-MAE: Self-Supervised Point Cloud Learning using Conditional Pseudo-Negative Embeddings
• Authors: Sutharsan Mahendren, Saimunur Rahman, Piotr Koniusz, Tharindu Fernando, Sridha Sridharan, Clinton Fookes, Peyman Moghadam,
• Abstract summary: PseudoNeg-MAE is a self-supervised learning framework that enhances global feature representation of point cloud mask autoencoders. We show that PseudoNeg-MAE achieves state-of-the-art performance on the ModelNet40 and ScanObjectNN datasets.
• Score: 55.55445978692678
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose PseudoNeg-MAE, a novel self-supervised learning framework that enhances global feature representation of point cloud mask autoencoder by making them both discriminative and sensitive to transformations. Traditional contrastive learning methods focus on achieving invariance, which can lead to the loss of valuable transformation-related information. In contrast, PseudoNeg-MAE explicitly models the relationship between original and transformed data points using a parametric network COPE, which learns the localized displacements caused by transformations within the latent space. However, jointly training COPE with the MAE leads to undesirable trivial solutions where COPE outputs collapse to an identity. To address this, we introduce a novel loss function incorporating pseudo-negatives, which effectively penalizes these trivial invariant solutions and promotes transformation sensitivity in the embeddings. We validate PseudoNeg-MAE on shape classification and relative pose estimation tasks, where PseudoNeg-MAE achieves state-of-the-art performance on the ModelNet40 and ScanObjectNN datasets under challenging evaluation protocols and demonstrates superior accuracy in estimating relative poses. These results show the effectiveness of PseudoNeg-MAE in learning discriminative and transformation-sensitive representations.

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