@@ -32,11 +32,11 @@ a part of the OpenMMLab project developed by [MMLab](http://mmlab.ie.cuhk.edu.hk
-**Support indoor/outdoor 3D detection out of box**
It directly supports popular indoor and outdoor 3D detection datasets, including ScanNet, SUNRGB-D, Waymo, nuScenes, Lyft, and KITTI.
For nuScenes dataset, we also support nuImages dataset.
For nuScenes dataset, we also support [nuImages dataset](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/nuimages).
-**Natural integration with 2D detection**
All the about **40+ methods, 300+ models**, and modules supported in [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/model_zoo.md) can be trained or used in this codebase.
All the about **50+ methods, 300+ models**, and modules supported in [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/model_zoo.md) can be trained or used in this codebase.
We support and provide some baseline results on [nuImages dataset](https://www.nuscenes.org/nuimages).
We follow the class mapping in nuScenes dataset, which maps the original categories into 10 foreground categories.
The baseline results include instance segmentation models, e.g., Mask R-CNN and Cascade Mask R-CNN.
The convert script can be found [here](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/data_converter/nuimage_converter.py).
The baseline results include instance segmentation models, e.g., Mask R-CNN, Cascade Mask R-CNN, and HTC.
We will support panoptic segmentation models in the future.
The dataset converted by the script of v0.6.0 only supports instance segmentation. Since v0.7.0, we also support to produce semantic segmentation mask of each image; thus, we can train HTC or semantic segmentation models using the dataset. To convert the nuImages dataset into COCO format, please use the command below:
