Mamba3D
This repository contains the official implementation of the paper:
[ACM MM 24] Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Model
📰 News
[2024/8] We release the training and evaluation code here! Pretrained weights are here!
[2024/7] Our MiniGPT-3D is also accepted by ACM MM24! We outperform existing large point-language models, using just about 1 day on 1 RTX 3090! Check it out!
[2024/7] Ours Mamba3D is accepted by ACM MM24!
[2024/4] We present Mamba3D, a state space model tailored for point cloud learning.
🥶 Code: https://github.com/xhanxu/Mamba3D
🐟 Pretrained weights:
You can find the pre-trained weights here.
Or, specifically as follows.
| Dataset | Pretrain | Acc | Weight |
|---|---|---|---|
| ShapeNet | Point-MAE | ckpt | |
| ModelNet40 | no | 93.4 | ckpt |
| ModelNet40 | Point-MAE | 94.7 | ckpt |
| ScanObjectNN-hardest | no | 91.81 | ckpt |
| ScanObjectNN-hardest | Point-MAE | 92.05 | ckpt |
😀 Contact
If you have any questions or are looking for cooperation in related fields, please contact Xu Han via xhanxu@hust.edu.cn.