Papers › ExpPoint-MAE: Better interpretability and performance for self-supervised point cloud...
ExpPoint-MAE: Better interpretability and performance for self-supervised point cloud transformers
Ioannis Romanelis, Vlassis Fotis, Konstantinos Moustakas, Adrian Munteanu
In this paper we delve into the properties of transformers, attained through self-supervision, in the point cloud domain. Specifically, we evaluate the effectiveness of Masked Autoencoding as a pretraining scheme, and explore Momentum Contrast as an alternative. In our study we investigate the impact of data quantity on the learned features, and uncover similarities in the transformer's behavior across domains. Through comprehensive visualiations, we observe that the transformer learns to attend to semantically meaningful regions, indicating that pretraining leads to a better understanding of the underlying geometry. Moreover, we examine the finetuning process and its effect on the learned representations. Based on that, we devise an unfreezing strategy which consistently outperforms our baseline without introducing any other modifications to the model or the training pipeline, and achieve state-of-the-art results in the classification task among transformer models.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Point Cloud Classification | ModelNet40 | ExpPoint-MAE | Overall Accuracy | 94.2 | #20 of 111 | Archive leaderboard | report |
| 3D Point Cloud Classification | ScanObjectNN | ExpPoint-MAE | OBJ-BG (OA) | 90.88 | #77 of 77 | Archive leaderboard | report |
| 3D Point Cloud Classification | ScanObjectNN | ExpPoint-MAE | OBJ-ONLY (OA) | 90.02 | #77 of 77 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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