Update README.md

README.md

@@ -17,7 +17,7 @@ TorchSparse is a high-performance neural network library for point cloud process
 Point cloud computation has become an increasingly more important workload for autonomous driving and other applications. Unlike dense 2D computation, point cloud convolution has **sparse** and **irregular** computation patterns and thus requires dedicated inference system support with specialized high-performance kernels. While existing point cloud deep learning libraries have developed different dataflows for convolution on point clouds, they assume a single dataflow throughout the execution of the entire model. In this work, we systematically analyze and improve existing dataflows. Our resulting system, TorchSparse, achieves **2.9x**, **3.3x**, **2.2x** and **1.7x** measured end-to-end speedup on an NVIDIA A100 GPU over the state-of-the-art MinkowskiEngine, SpConv 1.2, TorchSparse (MLSys) and SpConv v2 in inference respectively. 
 
 ## News
-**\[2024/11\]** TorchSparse++ has been integerated to [MMDetection3D](https://github.com/open-mmlab/mmdetection3d) and [OpenPCDet](https://github.com/open-mmlab/OpenPCDet) in a plugin style! [A full demo](./examples/) is available.
+**\[2024/11\]** TorchSparse++ is now supporting [MMDetection3D](https://github.com/open-mmlab/mmdetection3d) and [OpenPCDet](https://github.com/open-mmlab/OpenPCDet) via plugins! [A full demo](./examples/) is available.
 
 **\[2023/11\]** TorchSparse++ has been adopted by [One-2-3-45++](https://arxiv.org/abs/2311.07885) from Prof. Hao Su's lab (UCSD) for 3D object generation!