Papers › Point2Vec for Self-Supervised Representation Learning on Point Clouds

Point2Vec for Self-Supervised Representation Learning on Point Clouds

29 Mar 2023arXiv:2303.16570archive 2025-07-28

Karim Abou Zeid, Jonas Schult, Alexander Hermans, Bastian Leibe

Recently, the self-supervised learning framework data2vec has shown inspiring performance for various modalities using a masked student-teacher approach. However, it remains open whether such a framework generalizes to the unique challenges of 3D point clouds. To answer this question, we extend data2vec to the point cloud domain and report encouraging results on several downstream tasks. In an in-depth analysis, we discover that the leakage of positional information reveals the overall object shape to the student even under heavy masking and thus hampers data2vec to learn strong representations for point clouds. We address this 3D-specific shortcoming by proposing point2vec, which unleashes the full potential of data2vec-like pre-training on point clouds. Our experiments show that point2vec outperforms other self-supervised methods on shape classification and few-shot learning on ModelNet40 and ScanObjectNN, while achieving competitive results on part segmentation on ShapeNetParts. These results suggest that the learned representations are strong and transferable, highlighting point2vec as a promising direction for self-supervised learning of point cloud representations.

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Tasks

3D Part Segmentation3D Point Cloud ClassificationFew-Shot 3D Point Cloud ClassificationFew-Shot LearningRepresentation LearningSelf-Supervised Learning

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
3D Part Segmentation ShapeNet-Part point2vec Class Average IoU 84.6 #30 of 67 Archive leaderboard report
3D Part Segmentation ShapeNet-Part point2vec Instance Average IoU 86.3 #30 of 67 Archive leaderboard report
3D Point Cloud Classification ModelNet40 point2vec Mean Accuracy 92.0 #5 of 111 Archive leaderboard report
3D Point Cloud Classification ModelNet40 point2vec Overall Accuracy 94.8 #5 of 111 Archive leaderboard report
3D Point Cloud Classification ScanObjectNN point2vec Mean Accuracy 86.0 #43 of 77 Archive leaderboard report
3D Point Cloud Classification ScanObjectNN point2vec OBJ-BG (OA) 91.2 #43 of 77 Archive leaderboard report
3D Point Cloud Classification ScanObjectNN point2vec OBJ-ONLY (OA) 90.4 #43 of 77 Archive leaderboard report
3D Point Cloud Classification ScanObjectNN point2vec Overall Accuracy 87.5 #43 of 77 Archive leaderboard report
Few-Shot 3D Point Cloud Classification ModelNet40 10-way (10-shot) point2vec Overall Accuracy 93.9 #6 of 31 Archive leaderboard report
Few-Shot 3D Point Cloud Classification ModelNet40 10-way (10-shot) point2vec Standard Deviation 4.1 #6 of 31 Archive leaderboard report
Few-Shot 3D Point Cloud Classification ModelNet40 10-way (20-shot) point2vec Overall Accuracy 95.8 #8 of 31 Archive leaderboard report
Few-Shot 3D Point Cloud Classification ModelNet40 10-way (20-shot) point2vec Standard Deviation 3.1 #8 of 31 Archive leaderboard report
Few-Shot 3D Point Cloud Classification ModelNet40 5-way (10-shot) point2vec Overall Accuracy 97.0 #12 of 30 Archive leaderboard report
Few-Shot 3D Point Cloud Classification ModelNet40 5-way (10-shot) point2vec Standard Deviation 2.8 #12 of 30 Archive leaderboard report
Few-Shot 3D Point Cloud Classification ModelNet40 5-way (20-shot) point2vec Overall Accuracy 98.7 #7 of 30 Archive leaderboard report
Few-Shot 3D Point Cloud Classification ModelNet40 5-way (20-shot) point2vec Standard Deviation 1.2 #7 of 30 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.

Methods

AdamLabel SmoothingTransformer

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