Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.02125 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910038125182976 |
|---|---|
| author | Bregeon, Germain Preda, Marius Ispas, Radu Zaharia, Titus |
| author_facet | Bregeon, Germain Preda, Marius Ispas, Radu Zaharia, Titus |
| contents | In this paper, we introduce a novel 3D mesh convolution-based autoencoder for geometry compression, able to deal with irregular mesh data without requiring neither preprocessing nor manifold/watertightness conditions. The proposed approach extracts meaningful latent representations by learning features directly from the mesh faces, while preserving connectivity through dedicated pooling and unpooling operations. The encoder compresses the input mesh into a compact base mesh space, which ensures that the latent space remains comparable. The decoder reconstructs the original connectivity and restores the compressed geometry to its full resolution. Extensive experiments on multi-class datasets demonstrate that our method outperforms state-of-the-art approaches in both 3D mesh geometry reconstruction and latent space classification tasks. Code available at: github.com/germainGB/MeshConv3D |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_02125 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A 3D mesh convolution-based autoencoder for geometry compression Bregeon, Germain Preda, Marius Ispas, Radu Zaharia, Titus Computer Vision and Pattern Recognition In this paper, we introduce a novel 3D mesh convolution-based autoencoder for geometry compression, able to deal with irregular mesh data without requiring neither preprocessing nor manifold/watertightness conditions. The proposed approach extracts meaningful latent representations by learning features directly from the mesh faces, while preserving connectivity through dedicated pooling and unpooling operations. The encoder compresses the input mesh into a compact base mesh space, which ensures that the latent space remains comparable. The decoder reconstructs the original connectivity and restores the compressed geometry to its full resolution. Extensive experiments on multi-class datasets demonstrate that our method outperforms state-of-the-art approaches in both 3D mesh geometry reconstruction and latent space classification tasks. Code available at: github.com/germainGB/MeshConv3D |
| title | A 3D mesh convolution-based autoencoder for geometry compression |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.02125 |