Summary of Mamba3d: Enhancing Local Features For 3d Point Cloud Analysis Via State Space Model, by Xu Han et al.
Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Model
by Xu Han, Yuan Tang, Zhaoxuan Wang, Xianzhi Li
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Mamba3D model, based on state space models (SSM), outperforms Transformer-based models in point cloud analysis while maintaining linear complexity. The new model is designed to enhance local feature extraction and achieves superior performance, efficiency, and scalability potential. To achieve this, Mamba3D incorporates a Local Norm Pooling (LNP) block for extracting local geometric features and a bidirectional SSM (bi-SSM) with token forward and backward SSMS operating on the feature channel. Experimental results show that Mamba3D surpasses Transformer-based counterparts and concurrent works in multiple tasks, including classification and object detection, with or without pre-training. Notably, Mamba3D achieves state-of-the-art (SoTA) performance of 92.6% on ScanObjectNN and 95.1% on ModelNet40. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new model called Mamba3D to analyze point clouds more efficiently. They made some changes to the original Transformer-based models to make them better at finding patterns in point clouds. This new model is much faster than the old ones and does a better job of recognizing shapes and objects. It’s like having a superpower for analyzing 3D data. |
Keywords
» Artificial intelligence » Classification » Feature extraction » Object detection » Token » Transformer
